Sparse array configurations for 10 physical sensors.
\r\n\tThis book plans to focus on elaborating recent advances in the field of electrodeposition with fruitful evidence. The feasibility of electrodeposition routes to fabricate different metals and their derivatives for various uses are to be explained in detail.
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Karuppasamy",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/8532.jpg",keywords:"Topic 1:Thin films\r\nKeywords: Electrodeposition, Anodic deposition, cathodic deposition, Reaction kinetics\r\nTopic 2: Metals and metal oxides \r\nKeywords: II-VI elements, copper oxide, Ferrous oxide, Silver oxide\r\nTopic 3: Chalcogens \r\nKeywords: metal dichalcogens\r\nTopic 4: Alloys\r\nKeywords: Metal alloys, Solid solution, Gold - copper, Iron - Cobalt",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"August 14th 2019",dateEndSecondStepPublish:"September 4th 2019",dateEndThirdStepPublish:"November 3rd 2019",dateEndFourthStepPublish:"January 22nd 2020",dateEndFifthStepPublish:"March 22nd 2020",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"3 years",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"199404",title:"Prof.",name:"Dhanasekaran",middleName:null,surname:"Vikraman",slug:"dhanasekaran-vikraman",fullName:"Dhanasekaran Vikraman",profilePictureURL:"https://mts.intechopen.com/storage/users/199404/images/system/199404.png",biography:"Dr. Dhanasekaran Vikraman is an assistant professor in the Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Korea. He received his bachelor’s degree from Mannai Rajagopalaswamy Government Arts College, Mannargudi, affiliated with Bharathidasan University, India. He completed his master’s degree and Ph.D. at the Department of Physics, Alagappa University, India. Later, he received a visiting scientist position at KIST, Korea; a Marie-Curie Experienced Researcher fellowship at the Department of Physics, Aristotle University of Thessaloniki, Greece; and post-doc positions at Sejong University and Ajou University, Korea. He has authored more than 180 international journal articles and 3 book chapters and edited several books. 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Later, he completed his Post-Doc positions at Department of Metallurgical Engineering and Materials Science, Indian Institute of Technology Bombay, India, and Department of Chemical Engineering, Sogang University, Seoul, South Korea. His research interests mainly focus on synthesis and characterization of nanostructured materials for energy storage and conversion device applications. 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It has been an evergreen topic that has fancied many researchers due to the sheer variety of its applications. Array Processing is a scientific field of study which involves the processing of information-bearing signals received by an array of sensors operating in an environment of interest [1], for example, on the ground, above ground or under water. An array has two or more sensors which are arranged in a specific geometrical layout. An array has better directional properties than an individual sensor.
Sensor arrays find application in diverse fields such as radar (radio detection and ranging), space exploration, sonar (sound navigation and ranging), seismology, chemical sensing, medical imaging, wireless communications, navigation, source localization etc. Depending on the application, the sensors could be antennas, microphones, hydrophones, geophones, ultrasonic probes etc. For example, hydrophone arrays are used in sonar; acoustic arrays are used for audio source localization; piezoelectric sensors are used in medical ultrasound; geophone arrays are used in seismology etc. More specifically, antenna arrays are used for electromagnetic applications such as radar, radio astronomy, remote sensing, wireless communications, positioning and navigation [2]. Exhaustive reading for the topic of array processing can be found in [3, 4, 5]. A mention of the multidisciplinary nature of this topic is given in the introductory chapter of [4] and final chapters of [3, 5]. A thorough coverage of phased array theory and array processing applications in the modern era is provided in [6]. Arrays can have different geometries – such as linear, planar, circular, hexagonal, spherical etc. An array spanning across more dimensions can extract more details about the scene of interest.
In many of the above applications, it is of primary interest to sense the directions from which the source signals impinge the array. The signal of interest may be an electromagnetic wave, a sound wave, an underwater acoustic wave, a gas diffused into the surroundings, the location of a tumor etc. The use of multiple sensors bestows the array with a sense of direction. Individual sensors cannot sense direction.
A uniform linear array (array in which the antenna elements are arranged along a straight line) with an inter-element spacing of
A uniform linear array (ULA) with
Direction of Arrival (DOA) estimation involves determining the directions/angles at which electromagnetic sources are located with reference to the receiver array or the directions from which electromagnetic signals strike the array [7]. DOA estimation methods have been classified into three broad approaches, namely, classical methods, subspace methods and maximum-likelihood (ML) methods [8]. A new class of DOA estimation algorithms based on compressed sensing (CS) and sparse recovery have received much attention in the recent past [9, 10, 11, 12]. Dealing with coherent arrivals is a main problem in DOA estimation of practical sources. Two signals are said to be coherent if one is a scaled and shifted version of the other. Multipath interference and intentional jamming are the main causes for sources being coherent to each other [13]. When coherent sources arrive at the array, the array covariance matrix becomes non-diagonal, singular and rank-deficient. That is, its rank would be less than the number of incoming signals [14]. Hence, when subspace methods are used for DOA estimation, an additional step of spatial smoothing would be needed to restore the rank of the covariance matrix. Also, a major drawback of subspace methods is that they need prior information about the number of source angles to be detected, which is often impossible in practical scenarios (as the number of sources is usually unknown, a priori). Akaike information criteria (AIC) test and/or minimum description length (MDL) test and their variants are generally used to estimate the number of sources beforehand. However, these methods are also susceptible to failure when the signals are coherent [15].
In a passive array signal processing system, the array of sensors just listens to the environment, as in passive sonar, radio astronomy and wireless communication. Contrarily, in an active array signal processing system, a transmitter is used to illuminate the environment and the array listens to the signal reflected by the environment and/or objects of interest, as in radar, active sonar and medical imaging [1]. Both passive and active DOA estimation are quite famous in array processing literature.
Antenna arrays offer better gain and directivity than single antennas. High directivity enables the array to confine its radiation or reception to certain directions. As the array size increases (i.e., as the number of array elements increase), so does its aperture. Arrays with wider apertures provide narrower beam-widths and finer angular resolutions than those with smaller apertures [16]. The spatial response or radiation pattern of the array indicates the directions in which the array radiates its energy or receives energy from. Due to its directional properties, an array is often regarded as a spatial filter [17].
An antenna array can serve two purposes. It can help (i) determine the directions from which source signals impinge the receiver (direction of arrival (DOA) estimation), and (ii) in focusing the radiation pattern towards certain directions based on the knowledge of desired and undesired signal directions (beamforming). Additionally, antenna arrays also offer electronic beam-steering, whereby the array’s main beam can be pointed towards a desired direction just by controlling the element phases, without the need for any mechanical movement of the antenna platform.
A static array is one whose response does not change over time. The performance of such an array may be degraded severely under situations such as interfering signals, clutter returns, deliberate electronic countermeasures, non-hostile interference and multipath propagation. An adaptive array has the ability to control its response based on changing conditions of the signal environment coupled with the knowledge of desired and undesired signal directions [2].
Beamforming is the process of obtaining sharply focused beams in a given direction by applying a suitable set of complex weights (element currents and phases) to each of the sensors. Adaptive beamforming (ABF) involves the continuous adjustment of these weights to track the changes in the signal environment using adaptive algorithms that are based on certain specific optimization criteria. Generally, the optimization criteria optimize some measure of system performance such as mean square error, variance or likelihood [7].
Element spacing in an array is of high importance as it influences the occurrence of grating lobes. Grating lobes are large undesired side lobes (that are similar to the main beam in stature) that appear in the radiation pattern and radiate the energy in unwanted directions. Ideally, an inter-element spacing of half wavelength is followed in order to avoid spatial aliasing and to prevent the formation of grating lobes. In applications that require a limited field-of-view (FoV), presence of grating lobes does not hinder the array operation if the scanning region is limited to the grating-lobe-free area. An example of this is found in automotive radar systems, where the FoV is limited to −45° to +45°. Hence, an inter-element spacing of 0.59λ is generally used. Even though grating lobes exist beyond
Beamforming is one of the key enabling technologies for 5G systems that operate in the millimeter wave frequency band [18, 19]. It is known that millimeter frequencies are more sensitive to blockages and path loss. Huge array gains and sharp directional beams obtained from antenna arrays housing hundreds of antennas can counteract the propagation effects of the millimeter channel. DOA estimation also assumes prominence in 5G as DOA-based beamforming is one of the main requirements for smart antennas [20].
Though there are many definitions of sparse arrays in the array processing literature, the focus of this book chapter would be only on grid-based sparse arrays.
In grid-based sparse arrays, the sensors are assumed to be located on the grid points defined by an integer multiple of the basic inter-element spacing. That is, the sensors are assumed to lie at
Different types of linear sparse arrays have been studied in the past. Minimum Redundancy Arrays (MRAs) have numerous useful properties and had been primarily studied in the past in relation to radio astronomy [21, 22]. MRAs have also been applied in digital communications [23, 24, 25]. Numerous properties and modern applications of MRAs can be found in [26, 27, 28].
The use of sparse or non-uniform arrays in close future communication systems such as fifth generation (5G) telecom and the Internet of Things (IoT) is appealing as sparse arrays need fewer active elements than ULAs and operate with little or no degradation in performance [29].
The area of sparse arrays is continuously evolving and there are at least 50 types of linear sparse arrays in the current literature. A comprehensive understanding of various sparse array configurations is essential in the current scenario as more and more sparse arrays are being rapidly introduced into the literature. To the best of our knowledge, a comprehensive review on the properties of 1D sparse arrays has not been taken up in the past (barring the works by Liu and Vaidyanathan, Cohen and Eldar). This is a sincere attempt to bring a few well-known sparse arrays under one roof so that their characteristics could be compared.
The rest of the chapter is organized as follows. Section 2 gives an introduction to linear sparse arrays. Section 3 explains some terminology related to sparse arrays and discusses the characteristics of a few well-known sparse arrays. Section 4 gives an overview of sparse arrays with special properties and also discusses the effect of sensor failures on array performance. Section 5 provides a few future directions and Section 6 concludes the chapter.
Sparse arrays have aperture widths equal to regular/filled/full arrays but are sparsely populated. They consist of voids that arise due to missing/inactive sensors. The vacancies are deliberately created and are woven into the design of the sparse array to achieve certain desired characteristics. The span of the array is called its aperture. It is the sum of all the inter-element spacings. A ULA with
Another advantage of sparse arrays is that they are less affected by mutual coupling compared to ULAs. Sparse arrays are generally analyzed in the co-array domain. A difference co-array (DCA) is formed from the physical sparse array by considering all the spatial lags (differences) that can be generated using the available sensors. A missing spatial lag forms a hole. The DCA should be hole-free as the presence of holes introduces ambiguity in the estimation of spatial correlation and hence spatial angles.
There are several types of linear sparse arrays such as minimum hole arrays (MHAs) or Golomb Arrays, minimum redundancy arrays (MRAs), co-prime arrays, nested arrays, super-nested arrays and so on. MRAs and MHAs have been widely used for interferometry in radio astronomy [6, 21, 22, 30, 31]. While MRAs and MHAs existed for more than five decades, other sparse arrays such as co-prime arrays, nested arrays and super-nested arrays have been introduced in the past decade [32, 33, 34]. A good review on the properties of these sparse arrays can be found in the initial sections of [27, 35]. The introduction of co-prime array in the past decade can be considered as a watershed moment which has opened doors for modern applications of linear sparse arrays. Following that, nested arrays were introduced. These arrays offer hole-free co-arrays and also have closed-form expressions to determine the sensor locations. Many variants of the co-prime array [36, 37, 38] and the nested array [39, 40] have been proposed in the recent past. These arrays either improve the aperture or reduce the number of sensors needed to obtain a given aperture or make the array more immune to mutual coupling or they increase the hole-free region in the DCA.
Following are the desirable characteristics of sparse arrays:
The chosen array should provide the largest aperture for a given number of sensors. It is well-known that arrays with larger apertures provide better resolution and DOA estimation accuracy.
The array should be least affected by mutual coupling.
The array should have a hole-free co-array to facilitate unambiguous parameter estimation using the entire span of the co-array.
It is desirable that the array has closed-form expressions for sensor positions. Otherwise, there should be a provision to obtain the sensor positions using a look-up table (LUT) or through tabulated entries.
In general, sparse arrays such as MRAs and MHAs are represented in the
Another common representation is in the form of a binary string of 1 s and 0 s which represent the presence or absence of a sensor element on the respective grid point. For example, in the representation
For a sparse array with sensors at
The distinct entries (
In simple terms, the difference set is obtained by subtracting all possible sensor positions in the given sparse array. This gives rise to spatial lags. The DCA is then formed by considering only the non-repeating (distinct) spatial lags. For example, a sparse array with sensors at {0, 1, 4, 6} can generate all spatial lags (differences) between 0 and 6, resulting in a difference set of {0, −1, −4, −6, 1, 0, −3, −5, 4, 3, 0, −2, 6, 5, 2, 0}. The DCA {−6, −5, −4, −3, −2, −1, 0, 1, 2, 3, 4, 5, 6} is obtained by sorting the numbers and retaining only the distinct elements. Note that the repeating lag of zero is considered only once.
The number of unique lags in the DCA of a sparse array gives the number of source angles that can be detected during DOA estimation (often known as the degrees of freedom (DOFs) offered by the sparse array). Unique lags are often used in the analysis of sparse arrays with holes in the DCA. The usefulness of arrays that have holes in the DCA is limited by the span of the central continuous portion of the DCA. Though there are methods [43, 44] that can extend the continuous portion of the DCA, they are computationally intense. A more useful parameter, namely, the uniform degrees of freedom (UDOF), which indicates the number of continuous entries in the DCA; is often used to avoid ambiguity (as it clearly specifies the hole-free span of the coarray) [43].
Defining
A unit spacing corresponds to a spatial lag of one and can be generated by any pair of sensors that are adjacent to each other. In other words, any two sensors that are half-wavelength apart from each other are said to have unit spacing. The concept of unit spacing is important in determining the role of mutual coupling on the array’s performance. Empirically, arrays that have a large number of adjacent sensors are more susceptible to the effects of mutual coupling than those with fewer unit spacings.
It is necessary to understand the structure of a ULA and the normalized sensor positions in order to comprehend the properties of grid-based sparse arrays.
As per definition, there would be no missing sensors in the ULA. Hence, the sensor positions are given by the set
MRAs are synthesized from a ULA by eliminating selected sensors such that the sensors thus retained are capable of generating all possible spacings between zero and a specified number [41]. A ULA has many sensor combinations that provide a given spatial lag (e.g., considering the sensor positions from 0 to 9, a spatial lag of 4 can be obtained using any of the redundant sensor pairs {9, 5}, {8, 4}, {7, 3}, {6, 2}, {5, 1} and {4, 0}). MRAs minimize this redundancy by carefully removing select sensors. An optimum MRA has sensors at positions that are just enough to provide all the spatial lags from 0 up to a maximum number
For arrays with more than four elements, it is not possible to get rid of the redundancy completely if one were to ensure all possible spatial lags. For example, in a 5-element MRA whose sensors are located at {0, 2, 5, 8, 9}, it can be seen that the available sensor pairs can generate all the spacings between 0 to 9. But in particular, a spacing of 3 can be obtained using the sensor pairs {5, 2} and {8, 5}. This leads to redundancy but is inevitable for large arrays.
Consider an
A value of
MRAs do not have closed-form expressions to determine the optimum sensor positions and have to be synthesized using exhaustive search mechanisms. The optimization problem to find sensor positions in MRAs is given by
where
MHAs are obtained by optimizing the sensor positions such that a given spatial lag is obtained at most once. In other words, the sensors should be placed such that the spatial lags generated by them are unique. No two sensor pairs shall generate the same lag. Additionally, it is not bothersome in MHAs even if the available sensor pairs cannot generate all the spacings between 0 and
Coincidentally, the definitions of a zero redundancy MRA and a perfect Golomb array (MHA) bear the same physical meaning. Arrays with fewer than four elements qualify both as zero redundancy MRAs as well as perfect MHAs (Eg: A 4-element array with sensors at {0, 1, 4, 6}). However, for arrays with more than four sensors, neither zero redundancy MRAs exist nor do perfect MHAs. MRAs and MHAs mean different things for arrays with more than four sensors.
The objective function to synthesize MHAs is described below. A Golomb ruler consists of a set of integers
The coprime array consists of two ULAs. It was one of the first sparse arrays introduced with closed-form expressions (CFE) for sensor positions. That is, the sensor locations or the sparse array configuration can be immediately obtained once the number of sensors is given without the need for any exhaustive search mechanisms. One ULA has
The overall co-prime array is
Nested arrays (NA) can provide hole-free DCAs and were introduced as an alternative to MRAs and as an improvement over co-prime arrays. Nested arrays are better than co-prime arrays as they provide hole-free co-arrays. They too provide CFEs for element positions when the number of sensors is known. Two ULAs are needed to obtain a nested array. The first ULA has
For example, in a 10-element NA,
Though nested arrays are better than co-prime arrays in terms of the ability to provide hole-free co-arrays, they are severely affected by mutual coupling. This is due to the dense ULA portion at the beginning (level 1). Super-nested arrays were introduced to overcome this drawback of nested arrays [34]. In super-nested arrays, the level 1 elements of the NA are re-arranged (interleaved) to different positions within the span of the array so that the number of sensors with unit spacing gets reduced, thereby making the array less susceptible to mutual coupling. A 10-element super-nested array has sensors at {0, 2, 4, 7, 9, 11, 17, 23, 28, 29}. It can be observed that the level 1 elements of the NA are interleaved to different positions. Super-nested arrays too provide the same aperture as nested arrays for a given number of sensors and have CFEs for element positions. The formulation of super-nested arrays is slightly complicated and is, therefore, not explained here.
As a continuation, augmented nested arrays (ANAs) [49] were formulated. In ANAs, the level 1 dense sub-array of NA is split into several parts and is re-arranged to the left and right of the level 2 sparse array. The design of ANAs is elegant as they provide larger apertures, higher DOFs, and are less susceptible to mutual coupling than nested and super-nested arrays.
An improved nested array (INA) that provides larger aperture than the nested array for the same number of sensors has been proposed [50]. The improved nested array has a hole-free co-array. However, like the original nested array, the Yang’s nested array is also vulnerable to the effects of mutual coupling as it too has a dense ULA portion at the beginning. This array has a total of
The values of
It follows that a 10-element Yang’s nested array has
An extended nested array was also proposed in 2016 [51]. However, it does not offer apertures as large as the Yang’s INA described here.
A nested array configuration that provides larger aperture than the two-level nested array has been recently proposed [52]. This array provides larger aperture than MRAs (obviously than nested, super-nested, improved nested, and co-prime arrays) for a given number of sensors. However, Huang’s nested array suffers from holes in the co-array. The construction is similar to that of the Yang’s nested array in that there is a level 1 ULA, level 2 ULA with increased spacing and a separate sensor at the end. However, the number of sensors at each level and the element spacing in level 2 determine the sensor locations and the overall behavior of the array.
The triply primed array (TPA) is the union of three ULAs with different inter-element spacings. Three mutually prime numbers
Table 1 lists out the optimum sensor positions for different 10-element linear sparse arrays. The optimum MHA configuration for 10 sensors has been obtained through table look-up [46]. The sensor positions are shown in Figure 2. The continuous part of the DCAs of these sparse arrays are shown in Table 2.
Type of sparse array | Sensor positions | Aperture |
---|---|---|
ULA | [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] | 9 |
MRA | [0, 1, 3, 6, 13, 20, 27, 31, 35, 36] | 36 |
MHA | [0, 1, 6, 10, 23, 26, 34, 41, 53, 55] | 55 |
Co-prime array | [0, 3, 5, 6, 9, 10, 12, 15, 20, 25] | 25 |
Nested array | [0, 1, 2, 3, 4, 5, 11, 17, 23, 29] | 29 |
Super-nested array | [0, 2, 4, 7, 9, 11, 17, 23, 28, 29] | 29 |
Yang’s Nested array | [0, 1, 2, 3, 4, 10, 16, 22, 28, 33] | 33 |
Huang’s Nested array | [0, 1, 2, 3, 7, 15, 23, 31, 39, 48] | 48 |
Triply Primed array | [0, 12, 15, 20, 24, 30, 36, 40, 45, 48] | 48 |
Sparse array configurations for 10 physical sensors.
Sensor positions for various sparse arrays (arrays are considered in the same order as listed in
Type of sparse array | Continuous part of the DCA without holes | Uniform DOFs |
---|---|---|
ULA | 19 | |
MRA | 73 | |
MHA | 71 | |
Co-prime array | 35 | |
Nested array | 59 | |
Super-nested array | 59 | |
Yang’s Nested array | 67 | |
Huang’s Nested array | 19 | |
Triple Primed array | [0] | 01 |
Continuous portion of the difference co-array of the sparse arrays listed in Table 1.
The proliferation of linear sparse arrays in the past decade has led to the development of coarray-based DOA estimation methods. Coarray methods are based on the concept of difference co-array (DCA) and are well-suited for angle estimation in sparse arrays. As the physical array has missing sensors due to the sparseness, the array correlation matrix does not represent a Toeplitz structure and is not suitable for estimation of spatial correlation. Therefore, the analysis is shifted to the co-array domain. Due to the continuity of the DCA, the co-array correlation matrix represents a complete Hermitian Toeplitz structure and can be used to estimate spatial angles. Co-array MUSIC algorithm is widely used for DOA estimation in sparse arrays [32, 33]. More recently, other algorithms such as the co-array root-MUSIC [54, 55] and the co-array ESPRIT [56] have been introduced. The Khatri Rao (KR-MUSIC) algorithm which is applicable only to quasi-stationary sources (i.e., the sources which can be assumed to be stationary for short time durations) was introduced prior to the co-array MUSIC [57]. Recently, many algorithms based on compressed sensing have been introduced for DOA estimation in sparse arrays [58, 59]. In summary, DOA estimation algorithms that operate (i) when the number of sources is unknown, (ii) in the presence of coherent arrivals, (iii) under unknown mutual coupling, (iv) under low signal-to-noise ratio (low SNR) conditions, (v) under low snapshot conditions, (vi) in the presence of non-uniform or random noise, and (vii) in a short computational time; are largely sought-after for practical applications [60, 61].
According to the principle of pattern multiplication, the overall array response is the product of the array factor and the element pattern. The element pattern for an isotropic antenna is unity. Hence, in an array of isotropic antenna elements, the far-field pattern depends only on the array factor. The array factor of sparse arrays is conveniently evaluated using the element positions
where,
To get further idea on the characteristics of sparse arrays, the array factors of a few prominent sparse arrays given in Table 1 were evaluated using Eq. (6) and plotted using MATLAB. Table 3 lists the sparse arrays considered and their 3-dB beamwidths. Figure 3 shows the plot of array factors of the considered sparse arrays. It can be observed that MRAs provide the narrowest main beam characteristics for a given number of sensors as they possess the largest aperture among all the sparse arrays considered here. It has to be noted that the MHA can offer even finer beamwidth than MRAs.
Type of Sparse Array | Observed HPBW |
---|---|
ULA | 10.3° |
MRA | 2.2° |
Nested | 3.1° |
Co-prime | 4.1° |
Super-nested | 2.9° |
Beamwidths for the 10-element sparse arrays listed in Table 1.
Array factors of sparse arrays listed in
It would be good to have a few more figures representing the weight functions of the above sparse arrays and their DOA estimation performance when used with co-array MUSIC. However, they are omitted from this chapter for lack of space.
Another important aspect that a designer must be aware of, is that there can exist many sensor configurations for a given aperture in the case of MRAs and MHAs. Each of these configurations may possess different radiation characteristics or sensitivity. For example, the first column in Table 4 shows a few MRA configurations M1 – M4, each of which has seven sensors and is capable of offering an aperture of
MRA configuration | Beamwidth ( | First Side lobe level (dB) |
---|---|---|
M1 - [0, 1, 2, 3, 7, 11, 15] | 5.73° | −6.24 |
M2 - [0, 1, 3, 6, 10, 14, 15] | 5.16° | −6.50 |
M3 - [0, 1, 4, 8, 13, 14, 15] | 4.93° | −5.26 |
M4 - [0, 2, 4, 5, 8, 14, 15] | 5.50° | −6.58 |
Beamwidths and PSLs for different MRAs with same aperture and same number of sensors.
Array factors of MRAs M1-M4 mentioned in
Hence, care must be exercised before choosing a given MRA configuration.
Many modifications to coprime arrays have been proposed in the Literature. For example, the thinned coprime array, the coprime array with compressed inter-element spacing (CACIS), coprime array with displaced subarrays (CADiS), coprime array with reduced sensors (CARS) etc. [37, 38, 62]. Most recent is the generalized thinned coprime array (GTCA) [63], of which, the above ones can be considered as special cases. A multi-level prime array which extends the concept of coprime arrays has also been proposed [36]. The three-level prime array (3LPA) is a special case of the multi-level prime array and should not be confused with the triply primed array described in Section 3.2.9.
Recently, a unified array geometry in the form of a generalized nested subarray (GNSA) was proposed [55]. The geometry has a nested structure with two prototype arrays A and B. Specifically, if the prototype arrays A and B are MRAs, then the geometry leads to the nested MRA (NMRA) thereby providing the largest sparse array and associated hole-free coarray. On the other hand, when A and B are ULAs, the design provides a sparse array with the least aperture and DOFs. It can be said that the GNSA has revolutionized the development of sparse arrays in recent times. The prototype arrays could be nested arrays or super-nested arrays, or for that matter, any sparse array that has a hole-free coarray [55].
Active sensing applications need hole-free sum co-arrays. Symmetric sparse arrays are also useful for certain applications. The concatenated nested array (CNA) is one such array which is obtained by appending the level 1 elements of the nested array just after the level 2 elements such that the overall array is symmetric [40]. However, this array heavily suffers from the effects of mutual coupling owing to the closely spaced elements at both the ends. The Interleaved Wichmann Array (IWA) was proposed to overcome the mutual coupling problem of the CNA by re-arranging the sensors such that the number of sensor pairs with unit spacing is reduced [39]. On similar lines, a nested structure using two CNAs, namely, the Kløve array has been introduced with hole-free sum and difference coarray and is suitable for active as well as passive sensing [64]. More recently, low redundancy arrays with nested arrays and Kløve-Mossige as basis were proposed with hole-free sum coarray [65]. The sum coarray is defined as
where all the cross summations between sensor positions are considered.
While the difference coarray approach is well suited for the DOA estimation of circular sources, many non-circular source signals exist in practice. For example, binary phase shift keying (BPSK), minimum shift keying (MSK), unbalanced quadrature phase shift keying (UQPSK) etc. Non-circular sources have non-zero pseudo covariances (non-zero ellipse covariance matrix) which can be used to enhance the aperture of the virtual array to further exploit the received information for parameter estimation [66, 67]. To fully leverage the special properties of non-circular sources, the DOA estimation is performed using the sum difference co-array (SDCA) which is defined as
where
The use of difference co-array along with the sum co-array increases the virtual array span and leads to increased DOFs than possible by using the DCA alone. In SDCA-based designs, the vectorized conjugate augmented MUSIC (VCAM) algorithm is generally used for DOA estimation [68].
One of the prominent designs for sparse arrays that are suitable for non-circular sources is the nested array with displaced subarray (NADiS) as it provides CFEs for element positions, virtual apertures, and DOFs. The NADiS array has a large central continuous portion in the SDCA thereby providing large uniform DOFs. However, the SDCA is not completely hole-free. As an improvement, sparse array for non-circular sources (SANC) array was proposed with a hole-free SDCA [69]. A drawback of the SANC array is that it has no CFE for element positions. Therefore, the sensor positions in SANC have to be determined through exhaustive searching. Though an improved nested array with SDCA (INAwSDCA) [68] was proposed recently, there is no comparison with the NADiS and SANC arrays. An ultimate design of the nested array for non-circular signals is the translated nested array [67] which has CFEs for sensor positions and provides larger apertures than the NADis and the SANC. In addition, the SDCA of translated nested array is hole-free.
In recent years, moving array platforms or array motion are being exploited to obtain higher DOFs in sparse arrays. The synthetic coprime array [70], dilated nested array [71] and the multi-level dilated nested array [72] are few examples of sparse array designs that leverage platform motion to fill the holes in the DCAs. This topic is pretty new and is widely being explored.
In an array of identical elements, the overall pattern depends on parameters such as the array geometry, inter-element spacing, amplitudes and phases of the individual elements and the inherent pattern of each element [16]. In most cases, the array geometry and the type of array elements is fixed. For example, assume a linear array with patch antennas. In such cases, only the spacing of elements, their relative amplitudes and phases can be altered to modify the array pattern.
Perturbations in any of these parameters can distort the array’s response. Worst of all is the partial or complete failure of one or more sensors in the array. Element failures in sensor arrays can cause distortions in the main beam, side lobe levels and null placements, thereby disrupting the normal functioning of the array. Fault diagnosis and fault compensation are needed to ensure smooth operation of arrays. Several methods have been reported in literature that can (i) identify the location of the faulty element(s) and (ii) compensate or restore the array response through suitable weighting of the remaining healthy antennas in the array. Consider a 10-element ULA with uniform feeding. Imagine that the seventh sensor fails. In this case, the algorithm should be able to identify the position of the failed element and also determine suitable weights to be applied to the remaining nine sensors so that the compensated pattern closely resembles the response of the healthy array. As the weights are no more uniform, the use of digital beamforming is called for. Many bio-inspired algorithms or compressed sensing techniques have been used in the past either to detect sensor failures or to compensate the pattern of a faulty array or for both [73, 74, 75, 76, 77, 78, 79, 80]. An extreme case and new perspective is presented in [81], where a sparse array is said to be formed when one or more elements of an ULA fail at random.
The notions of robustness, fragility, essentialness etc., in relation to sparse sensor arrays have been introduced in recent years by Liu and Vaidyanathan [82, 83, 84]. The fragility of a sparse array gives a measure of how vulnerable the array is to its’ sensor failures. Fragility is defined as the number of essential sensors to the total number of sensors in the sparse array. A sensor is said to be essential if its failure/absence alters the difference coarray or introduces holes into the coarray. Arrays in which all sensors are essential are known as maximally economic sparse arrays (MESA). MRAs, nested and super nested arrays are maximally economic as all their sensors are essential. For this reason, these arrays are highly fragile with a fragility of
It is well-known in MRA theory that, in an array of
As an example, a 10-element RMRA has sensors at {0, 1, 2, 6, 7, 11, 15, 16, 18, 19} [85]. The weight function of the RMRA is plotted in Figure 5. It can be seen that all the spatial lags from
Weight function of the 10-element RMRA described above.
Consider a situation where a particular element in the above RMRA fails (say the element at position 11). The weight function of the RMRA with failed sensor is shown in Figure 6. It can be observed that the weights of a few spatial lags fall down to one but none of them becomes zero. As long as there is just a single-element failure in RMRAs, the weight of any given spatial lag never becomes zero, meaning that holes would never occur in the DCA. This justifies the robustness of RMRAs.
Weight function of the RMRA with failed sensor at position 11.
In recent years, sparse arrays based on fractal geometries have been proposed. Such arrays use a small sparse array as a base (called generator) to obtain larger sparse arrays through recursive formulations. Examples include the Cantor arrays proposed by Liu and Vaidyanathan and the generalized fractal sparse arrays proposed by Cohen and Eldar [35, 87].
Any review of linear sparse arrays would be incomplete without a mention of their usage in the design of sparse MIMO radar arrays. MIMO radar arrays make use of co-located transmit and receive arrays. These transmit and receive arrays work in tandem and produce the effect of a large virtual array. The transmitting array consists of
Following the introduction of the minimum redundancy MIMO radar [45] in 2008, several other sparse MIMO configurations have been proposed in the literature [89, 90, 91, 92, 93]. However, a thorough review of the properties of sparse MIMO arrays is beyond the scope of this article. However, it is easy to foresee that if the transmit and receive arrays are maximally sparse with hole-free co-arrays (like MRAs), the resulting MIMO radar array would also be highly sparse and, therefore, capable of providing the largest virtual array aperture for a given number of sensors and hence the highest DOFs as reported recently [94]. Several novel designs of sparse MIMO radar arrays are being proposed. Another research area which has gained traction in recent years is the co-existent MIMO radar and MIMO communications.
Evolutionary and swarm-based algorithms have been extensively applied for array pattern optimization. The No-free-lunch (NFL) theorem in optimization theory says that there is no single optimization algorithm that works well against all the optimization problems (objective functions). Going by the above fact, it might so turn out that there might exist a few algorithms which could be more suitable over others for synthesizing MRAs and MHAs. As many powerful nature inspired optimization algorithms (grey wolf optimizer, moth flame optimizer, whale optimization, sparrow search algorithm and other hybrid meta-heuristic approaches etc. [95, 96, 97, 98]) have been introduced in the recent past, it would be a worthwhile effort to try synthesizing MRAs and MHAs using such algorithms. One such attempt to determine large MRAs using parallel processing has been recently reported [99]. In recent times, deep learning methods are being employed to synthesize sparse arrays for joint requirements such as hole-free coarrays, low peak side lobes, and optimum far-field performance [100, 101].
In the future, research could be done to determine large RMRAs such that tabulated entries on the optimum RMRA configurations for a given number of sensors could be widely made available to the scientific community. Similarly, efforts could be made to find robust nested arrays with closed-form expressions for sensor positions as an alternative to RMRAs [33, 85].
In sparse arrays, beamforming is usually performed in the co-array domain [102, 103, 104]. The weights of the virtual sensors in the coarray are adjusted to obtain the desired beam pattern. It would be interesting to see how the failure of one or more sensors in the physical array affects these beamforming weights. Detection of failed sensors in the physical array and subsequent compensation of the beam pattern in the coarray are a few open research challenges for sparse array analysis.
In this chapter, the properties of various linear sparse arrays have been compared. It is found that different sparse arrays have different design criteria and trade-offs. It remains as a future scope to synthesize new MRAs and RMRAs using the latest bio-inspired and/or deep learning algorithms. Antenna array processing techniques will be in the limelight for many years to come as many future wireless communication systems (infrastructure-based and ad hoc) heavily rely on them. DOA estimation algorithms and adaptive beamforming methods using sparse/full arrays in the presence of array miscalibrations/failures are a trending research topic at the moment. It is believed that this chapter serves as a comprehensive guide to new researchers in the field of sparse array signal processing.
This work was not funded by any agency or grant.
There are no conflicts of interest.
My thanks to Dr. Chun-Lin Liu, Assistant Professor, Department of Electrical Engineering, National Taiwan University for providing wide open access to his MATLAB codes on co-array MUSIC and super-nested arrays.
Nigeria has the largest mangrove forest in Africa and the third largest contiguous mangrove forest in the world. The mangrove region is widest on the sides of the Niger Delta, 35–45 km and narrows towards the centre to a width of 15 km except for the channel of the Brass River, which has extensive mangroves far upstream [1].
The mangrove forests of Nigeria comprise principally only three families and six species as follows: Family,
Anthropogenic disturbances in the Niger Delta include oil spills which has been extensive, difficult to assess and often underreported. According to [3], oil spillages in the Niger Delta most especially in the mangrove wetlands are never reported or merely branded minor without minimum post-spill containment, recovery and remediation responses. Also, the World Commission on Environment and Development (WCED) as cited by [4] had reported that the mangrove deforestation and degradation are some of the greatest factors that would cause species extinction in the Niger Delta region in the next 50 years. Consequently, it is frightening that depletion of the mangrove forest could eliminate 5–15% of species by 2020 [5]. The Niger Delta biodiversity, including the Ijala-Ikeren wetland is very important for the concomitant presence of rainforests, mangroves, and many endemic flora and fauna, which are affected by oil pollution from oil spills, usually from bunkering and pipeline vandalisation of the petroleum refinery facilities situated less than 2 km to the East of the Ijala-Ikeren Wetland as well as solid waste pollution.
Oil pollution and its attendant impact on the creek ecology and the community are highly visible in the mangrove swamps due to proximity of the communities to refinery and oil pipelines criss-crossing the environment. The Ijala-Ikeren wetland has been affected by industrial activities, and pollution and this has affected the water quality in the community, and arthropods (insects) and amphibians [6]. Reported that the physical and sanitary quality of hand-dug well waters from communities around the Warri refinery including Ijala-Ikeren wetland was found slightly acidic at 6.04 and may be attributed to emissions from gas flaring and petroleum refining activities, which is common in the area. They also stated that the water quality from the community’s surface water bodies and aquatic insect indicators are believed to be gradually deteriorating.
The Ijala-Ikeren wetland is well watered all the year round although the level of water in the creeks drops to about two meters during the dry season and varies daily owing to tides. The current ecological regime in the wetland is the amount of water in creeks and creeklets. This water is determined by tidal flow of sea water from the Atlantic Ocean at high tide and fresh water from the mainland into the Ocean at low tide. The continuous mixing of fresh and sea water gives rise to the brackish nature of water in the creeks and creeklets and in the mangrove swamp. Any change in the water regime either through the stoppage of sea water flowing into the wetland or fresh water flowing into the Ocean, will certainly change the ecology of the wetland and its current composition. The potential route of off-site contaminants to the ecosystem is by Ijala-Ikeren creeks and creeklets. The discharge of water from the creeks to the mangrove ecosystem is constant and runs all through the year but quantity and volume of water available in the mangrove and creeks are unstable [7]. Higher volumes of water are available in the rainy season due to run-offs and inter-tidal nature of the Warri River especially during high tide (personal communication).
Aquatic insects play very important role in food webs, and particularly in wetlands, the larval and adult stages provide a wholesome meal to fish [7], shorebirds [8] and other creatures in the Niger Delta mangroves. They are essential elements in food webs, essentially in energy flow and nutrient cycling [9]. This food source is key to the survival strategy of some fishes, amphibians and other animals. Selected aquatic insects are predators and in consuming their watery prey they help keep the insect population stable and functioning. Many species of aquatic insects are very sensitive to pollution and other environmental hazards, thus they reflect environmental changes and as a result are often used as indicators of the effects of human activity on water system and provide information on habitat and water quality [10]. Also some aquatic insects help keep the water clear by filtering it, others support in breaking down fallen dead leaves, while yet others clear the algae, allowing the water to produce more oxygen [11, 12].
Amphibians have important roles within nature and their sensitivity to changing environmental conditions may help determine the health of an ecosystem. They constitute an important part of food chain and contain important pharmaceutical compounds on their skin [13]. The International Union of Conservation of Nature (IUCN) Red List of threatened species has identified amphibians as being the most threatened vertebrate group, with about 40% at risk of extinction. The extinction and decline have been variously attributed to habitat loss, pollution, climate change, wild fire and over-exploitation [14].
Recent studies have documented the incidence and severity of sediment contamination by heavy metals in mangrove ecosystems [15, 16]. Contaminants are released via industrial activities and ultimately enter aquatic ecosystems [16, 17].
Industrial effluent is a common anthropogenic impact on aquatic ecosystems including mangroves and this kind of pollution changes physical and chemical characteristics of aquatic systems, thus affecting the assemblage of aquatic fauna [17, 18].
The mangrove forests and salt marshes control the tidal wetland ecosystem dynamics and contribute to the development and condition of the tidal saline wetland ecological communities [19]. Mangrove forests and salt marshes are highly valued ecosystems; however, mangrove forests and salt marshes maintain divergent ecological communities and a different suite of goods and services [20]. Climate change, environmental stress and anthropogenic activities can cause the conversion of mangrove forest to salt marshes, with positive and negative expected and unexpected consequences (e.g., effects upon fish and wildlife populations, ecosystem resilience, nutrient cycling, and variation of texture and soil geochemistry). The physicochemical conditions and ecological implications of these mangrove forests to salt marshes conversions are poorly understood, but would likely include changes in soil, water, associated wildlife populations and supply of some ecosystem goods and services [21, 22].
The lack of publications on the biodiversity (particularly on the indicator species)—such as the insects and amphibians in Ijala-Ikeren wetlands of the Niger Delta, necessitated this study. Hence, this preliminary study was aimed at evaluating the abundance, community structure and diversity of biodiversity therein coupled with temperature, salinity and the pH levels of the ecosystem. We therefore, welcome future collaboration with available funding for a robust study.
The study was conducted in Ijala-Ikeren wetland located within latitude 05.55°N and 05.57°N and longitude 05.68°E and 05.70°E in Warri, Delta State, Nigeria (Figure 1). The wetland is a brackish network of creeks and creeklets, and marshes with one or more relatively narrow connections to the sea.
Map of the study area.
The study area which covers a space of 1 km2 was divided into 2 regions: interior region bearing three sites; site 1, 2 and 3, and edges (fringes) region bearing sites 4 and 5. Altogether, 5 sites were engaged. Site 1 is located at the position, 05.55940°N, and 05.69630°E. The substratum here was covered with an admixture of black muddy and clayey sediment with an average depth of 0.2 m. Tidal flow was low and was at a low transparency. Vegetation here includes
Physicochemical parameters such as air temperature, water temperature, and pH were determined in situ. Temperatures were measured using the 0–100°C mercury in glass thermometer (Kurison Model—59). The pH was determined in situ using digital pH meter (consort 121, Belgium) adopted from [23]. While, salinity was determined in the laboratory by using HACHCO150 Model for total dissolved solid/conductivity/salinity meter.
A description of the vegetation cover in and around the wetland was undertaken. Diversity of species of mangroves was recorded from the two studied regions. We used a [24] sampling technique for rapid assessment of the mangal vegetation of the brackish ecosystem in the study area by walking around at low tide. This involved a reconnaissance survey of the study stations of the Ijala-Ikeren mangrove ecosystem where a systematic sampling along the directed transects was applied to conduct the flora survey of the study sites.
Insect sampling was conducted in the dry season (October 2013—February, 2014) and in the wet season (March–June, 2014). It was a 9 month study. Collection of samples was carried out through the use of sweep and kicks nets, and hand collection. Access to the mangrove interiors was through Falcorp Mangrove Park. Hand collections were made from wetland (mangrove) plants and from ant hive (Figure 2). The sieve-like dip net was used to collect aquatic insects from a distance on the mangroves while small aquatic nets were used to collect specimens in the aquatic plant (
An ant hive hanging on a mangrove plant in Ijala-Ikeren mangrove swamp where insect collection was made.
Kick sampling method modified after [23] was used to collect aquatic insect larvae (macro-invertebrates) for 3 min. We used 1 min to conduct searches before disturbing the water column and placing the net against the direction of the current for actual sampling. This method was also used to collect insects from water surface, from boulders, logs of wood and plants (Figure 3). All the collected specimens were identified using keys of [25, 26, 27]. The identified specimens were counted and placed into vials of 70% ethanol (C2H6O) which were well labelled indicating the location and date of collection.
Insect collection from under woods, logs and fallen leaves in Ijala-Ikeren mangrove.
Sampling for amphibian was conducted using the Visual Acoustic Encounter Survey (VAES) method (i.e. listening to amphibians call and tracing the calls) at night and the anurans were captured by handpicking. The method was used along the trunk road (edges) traversing the area and in the interiors wetland (mangrove swamp) through the access to Falcorp Mangrove Park. Collection at the park was both during the day and at night.
We used empirical method to ascertain the occurrences and diversity of fish species within Ijala-Ikeren wetland from the local fishermen at a few fish landings therein. A few fish species were equally sighted, observed and noted in-situ.
Overall diversity of soil insect was expressed as species richness, abundance, evenness and Simpson diversity index. Species richness and diversity patterns are a fundamental point for any scientific act in conservation biology [28]. Species richness represents the simplicity of describing communities at different scales and its broadly understood meaning. The complementary picture of overall diversity pattern of any community was documented by information about the abundance of species, the evenness of communities across the sampling region or the dominant species. Abundance is the kind of diversity measures that has inclusively been considered as equivalent to biodiversity per se [29] and referred to the sum of individuals in area. However, Simpson diversity index (D) is nearly the most tractable and statistically useful calculation [30]:
Where D is Simpson diversity index, λ is an index of dominance. pi is the proportion of the community occupied by the ith species.
All parameters of diversity were calculated with PAST (version. 1.92) software running on Windows® XP, [31].
The mean values of physical parameters were recorded at the study sites. Air temperature ranged between 29 and 29.50°C in the mangroves (interior) and 30°C at the fringes (edges), while water temperature was between 28.50 and 29°C in the interior and 28–29°C at the edges (Table 1). On the other hand, the measured pH values ranged between 4.8 and 4.9 in the interior and 5.3–6.1 at the edges, and salinity values were between 1.50 and 1.70 ppm (Table 1).
Parameter | Site 1 (Ijala-Ikeren mangrove) | Site 2 (Ijala-Ikeren mangrove) | Site 3 (Ijala-Ikeren mangrove) | Site 4 (pond infested with water hyacinth at the fringe of mangrove) | Site 5 (backhouse pond close to the boundary of the mangrove swamp) |
---|---|---|---|---|---|
Air temperature (°C) | 28.00 | 29.00 | 29.50 | 30.00 | 30.00 |
Water temperature (°C) | 29.00 | 29.00 | 28.50 | 28.00 | 29.00 |
pH | 4.8 | 4.7 | 4.9 | 5.3 | 6.1 |
Salinity (ppm) | 1.55 | 1.50 | 1.70 | 1.70 | 1.58 |
Mean values of physical parameters of sampled sites in Ijala-Ikeren mangrove swamp and fringes.
The study also included the description of the vegetation in and around the wetland. It showed that the mangrove swamp in Ijala-Ikeren wetland is covered by three species of
S/N | Common name | Scientific name | Habitat | Uses |
---|---|---|---|---|
1 | Red mangrove | Mangrove swamps | Fuel wood, timber | |
2 | Red mangrove | Mangrove swamps | Fuel wood, timber | |
3 | Red mangrove | Mangrove swamps | Fuel wood, timber | |
4 | White mangrove | Mangrove swamps | Fuel wood, timber | |
5 | Screw pine | Mangrove swamps | — | |
6 | Water hyacinth | Fringes of mangrove swamp Floating in ponds | — |
List of identified flora in Ijala-Ikeren mangrove swamp and fringes.
The sampled insects were identified into three orders: Hemiptera, Hymenoptera and Diptera. As shown in Table 3,
Order | Family | Species | Site 1 (Ijala-Ikeren mangrove) | Site 2 (Ijala-Ikeren mangrove) | Site 3 (Ijala-Ikeren mangrove) | Site 4 (pond infested with Water hyacinth at the fringe of mangrove) | Site 5 (backhouse pond close to the boundary of the mangrove swamp) |
---|---|---|---|---|---|---|---|
Hemiptera | Gerridae | 5 | 8 | 9 | — | — | |
Hymenoptera | Formicidae | 25 | 45 | 37 | — | — | |
Diptera | Chironomidae | — | — | — | 17 | 25 | |
Total no. of taxa | 2 | 2 | 2 | 1 | 1 | ||
Total abundance | 30 | 53 | 46 | 17 | 17 | ||
Margalef’s index (d) | 0.29 | 0.25 | 0.26 | ||||
Shannon diversity index (H′) | 0.45 | 0.42 | 0.49 | ||||
Evenness (E′) | 0.65 | 0.61 | 0.71 |
Biodiversity index (ecological diversity), abundance and distribution of insect species encountered in Ijala-Ikeren mangrove swamp and fringes.
Spatially, the three study sites in the interior (mangrove swamp) represented the highest percentage (75.5%) of species abundance, where
As shown in Table 3, the study sites at the mangrove swamp were the richest area with species number (2 species), site 1 had the greatest value in Margalef’s species richness index (d) and diversity of species out of these sites. On the other hand, site 2 had the least value of both indices.
A total of ninety-one (91) individual amphibians were collected in this study. Thirteen amphibian species belonging to three different families were identified: Bufonidae, Hyperoliidae and Ranidae. All these species were collected from the edges (fringes) of the mangrove swamp (Table 4).
Family | Species | Abundance | Family abundance % |
---|---|---|---|
Bufonidae | 3 | 3.30% | |
Hyperoliidae | 23 | 60.40% | |
2 | |||
1 | |||
13 | |||
2 | |||
1 | |||
12 | |||
1 | |||
Ranidae | 6 | 36.3 | |
4 | |||
9 | |||
1 | |||
Total no. of taxa | 13 | ||
Total abundance | 91 | ||
Margalef’s index (d) | 2.75 | ||
Shannon diversity index (H′) | 2.08 | ||
Evenness (E′) | 0.08 |
Abundance and distribution of amphibian fauna encountered in Ijala-Ikeren mangrove swamp and fringes.
The ‘E’ which was very manifest in the unidentified Hyperolid (
Hyperoliidae was the most abundant one among the identified species (eight taxa) with an abundance of fifty-five individuals species. One feature of this group was the tendency to present different morphs (colour shade, pigmentation and design). Among this group, the dominant species was
A checklist of the fish species is presented in Table 5. Catfish and Tilapia were the dominant species in the entire study area. A total of twenty representative species were recorded throughout.
S/N | Common name | Scientific name |
---|---|---|
1. | Trunkfish | |
2. | African Bonny Tongue | |
3. | Characin | |
4. | Moonfish | |
5. | Cyprinit-African Carp | |
6. | — | |
7. | Catfish | |
8. | Silver Catfish | |
9. | Catfish | |
10. | Electric Fish | |
11. | Niger Perch | |
12. | Tilapia | |
13. | Tilania | |
14. | Tilapia | |
15. | Snakehead | |
16. | — | |
17. | Clarias | |
18. | — | |
19. | Elephant Snout Fish | |
20. | — |
Indicative checklist of the fishes found in Ijala-Ikeren wetland.
The study of vegetation in Ijala-Ikeren wetland clarified the richness of mangrove species, which is covered by about 95% Rhizophora species (red mangrove) and
The acidity of water was explained by [6] who investigated the impact of refinery effluents around Warri Refinery and Petrochemical Company (WRPC) on surface waters of the creeks and soil qualities of host communities within and around Ijala-Ikeren wetland, and concluded the change in water pH to be 6.3, while the soil pH was 6.8 in Ijala-Ikeren wetland. This inference may be related to the discharge of these effluents from Warri Refinery and Petrochemical Company (WRPC) into Warri River which feeds the Ijala waters and also crude oil and its fraction seepages from numerous loading and off-loading jetties within the river which finds its way into the creeks in the Ijala-Ikeren wetland. They further stated that the Warri River is open to flooding which carries various contaminants. Contrary to our findings in water pH, the sediments studies conducted elsewhere in the Mexican pacific and Thailand, recorded alkaline pH values ranging from 6.91 to 7.83 [18, 22].
Secondly, the discovery of illegal toxic waste dumpsites belonging to WRPC within these wetlands (pers. comm.) could increase the pollutant load of the study area. In turn, the highest concentration of calcium and salinity level was reported at the surface water within the wetland as reported by [6] though we did not analyse for calcium and heavy metals in this study. Also, heavy metals in varying concentrations and reasonable quantity from refinery effluents that resulted from Nigeria’s crude oil could be one of the contributing factors to the acidic pH values. Especially, the metallic components in crude oil which are in the form of metalloporphyrin chelates, transition metal complexes, organometallic compounds, carbonyl acid salts of polar functional groups and colloidal minerals while other inorganic constituents of crude oil are sulphur, nitrogen, and oxygen [33].
Spatially, the study sites at the mangrove swamp was the richest habitat in terms of species number of insects, site 1 had the greatest value in Margalef’s species richness index (d) and diversity of species out of these sites. This result may relate to increasing the renewal of water during high tide as a common phenomenon in the area which could give rise to runoffs of debris from land into the mangrove. On the other hand, site 2 had the least value of both indices. As expected, this study site was characterised by its proximity to oil pipelines which over time had ruptured severely due to vandalisation and outlet that leads into the creeks.
Among the sampled insects, the presence of
Many amphibian species and their eggs are unable to tolerate and thrive in the brackish/salty environments of the mangrove (interior). One species,
So, the presence of
The presence of the insect (
Species richness measures the number of different kinds of species present in a particular area. Margalef’s Species Richness index (d) values range from 1 to 5 where the higher index indicates the greater diversity (Table 3). Shannon diversity index (H′) takes into account the number of species and the evenness of the species. Diversity (H′) values less than 1.0 are considered very low; between 1.0 and 3.0 have moderate diversity, while any value greater than 3.0 signifies high diversity and ecosystem stability (i.e. similar to a pristine ecosystem). Evenness (E′) refers to how evenly each species is represented in a given ecosystem. Equitability assumes a value between 0 and 1 with 1 being complete evenness.
The abundance was very evident in the order Hymenopteran (
Oil pollution from oil spills usually from illegal bunkering and pipeline vandalisation of the petroleum refinery facilities situated less than 2 km to the East of the wetland, and solid waste pollution are the major threats to this unique wetland. The Ijala-Ikeren wetland ecosystem has been affected by industrial activities and pollution, and these have affected the insect species and the amphibians. Based on the findings of this study, it has been established that the wetland ecosystem is highly stressed from contaminants with records of high acidic pH values which has resulted in the absence of aquatic insects and reduced fishes. The study pointed out that over 5 years (between 2009 and 2014) drastic changes in pH values occurred in the wetlands, as it reduced from 6.3 in 2009 to 4.8 in 2014. This could deteriorate more to a very precarious situation in the next 5 years if the pollution source is still left unattended to.
The study has shown that amphibian population in Ijala-Ikeren wetland appeared not to be under threat or extinction in spite of their absence at the mangroves; as thirteen (13) species were encountered with some equally having preference for acidic ponds. The results also showed that degradation of the wetland is on-going and it will be recommended that wetland conservation should be encouraged to forestall further loss of biodiversity. In doing this, adequate channels to set up a national awareness campaign among coastal and marine communities in the Niger Delta in conserving wetlands be identified. This becomes necessary as there are over nine (9) enacted national legislation/Acts for the protection of wetlands but their enforcement has been very weak. So government needs to mobilise in order to save our wetlands.
Government and non-governmental agencies should encourage studies on the inventory of aquatic insect species of water bodies in the host communities around the refinery to determine the health and pollution status of the aquatic ecosystem via funding.
The funding for this research was provided by the Duke University Marine Laboratory/Oak Foundation Mini-Grant for marine conservation project to the first author (MAU). We are very much grateful to Duke University Marine Lab at Beaufort, NC USA. Thanks to Mr. Henry Erikowa, managing director of Falcorp Mangrove Park, Ijala-Ikeren in Warri for providing logistics for the team during the study. Special thanks also go to Messrs. Abiloye Erikowa and Festus Arijode for their various field assistance.
The authors have not declared any conflict of interests.
(A–J) Anurans species encountered at Ijala-Ikeren community. (A)
(K–M) Insect species encountered at Ijala-Ikeren community. (K)
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