Stochastic Observation Optimization on the Basis of the Generalized Probabilistic Criteria

Till now the synthesis problem of the optimum control of the observation process has been considered and solved satisfactorily basically for the linear stochastic objects and observers by optimization of the quadratic criterion of quality expressed, as a rule, through the a posteriori dispersion matrix [1-4]. At the same time, the statement of the synthesis problem for the optimum observation control in a more general case assumes, first, a nonlinear character of the object and observer, and, second, the application of the non-quadratic criteria of quality, which, basically, can provide the potentially large estimation accuracy[3-6].


Introduction
Till now the synthesis problem of the optimum control of the observation process has been considered and solved satisfactorily basically for the linear stochastic objects and observers by optimization of the quadratic criterion of quality expressed, as a rule, through the a posteriori dispersion matrix [1][2][3][4]. At the same time, the statement of the synthesis problem for the optimum observation control in a more general case assumes, first, a nonlinear character of the object and observer, and, second, the application of the non-quadratic criteria of quality, which, basically, can provide the potentially large estimation accuracy [3][4][5][6].
In connection with the fact that the solution of the given problem in such a statement generalizing the existing approaches, represents the obvious interest, we formulate it more particularly as follows.

Description of the task
Let the Markovian vector process t, described generally by the nonlinear stochastic differential equation in the symmetrized form where f, f0 are known N -dimensional vector and N  M -dimensional matrix nonlinear functions; nt is the white Gaussian normalized M -dimensional vector -noise; be observed by means of the vector nonlinear observer of form: where Z -L  N -dimensional vector of the output signals of the meter;

h(,t) -a known nonlinear L-dimension vector -function of observation;
Wt -a white Gaussian L-dimension vector -noise of measurement with the zero average and the matrix of intensity W D .
The function of the a posteriori probability density (APD) of process As the main problem of the a posteriori analysis of the observable process t is the obtaining of the maximum reliable information about it, then the synthesis problem of the optimum observer would be natural to formulate as the definition of the form of the functional  * is some bounded set of the state parameters t.
In the final forming of structure of the criterion of optimality J it is necessary to take into account the limited opportunities of the practical realization of the function of observation h(,t), as well, that results, in its turn, in the additional restriction on the choice of functional dependence h(,t). The formalization of the given restriction, for example, in the form of the requirement of the minimization of the integrated deviation of function Н from the given form Н0 on interval *  during time interval Т allows to write down analytically the form of the minimized criterion J as follows: Thus, the final statement of the synthesis problem of the optimum observer in view of the above mentioned reasoning consists in defining function h(,t), giving the minimum to functional (2).

Synthesis of observations optimal control
Function APD, included in it, is described explicitly by the integro-differential Stratonovich equation with the right-hand part dependent on h(,t). The analysis of the experience of the instrument realization of the meters shows, that their synthesis consists, in essence, in defining the parameters of some functional series, approximating the output characteristic of the device projected with the given degree of accuracy. As such a series one uses, as a rule, the final expansion of the nonlinear components of vector h(,t) in some given system of the multidimensional functions: power, orthogonal etc.
Having designated vector of the multidimensional functions as 1 ... For the subsequent analytical synthesis of optimum vector -function h(,t) in form of (3) we rewrite the equation of the APD (,t) in the appropriate form 1 2 , , , The constructions carried out the problem of search of optimum vector h(,t) is reduced to the synthesis of the optimum in-the-sense -of-(2) control h of the process with the distributed parameters described by Stratonovich equation (in view of representing vector Н0(,t) in the form similar to (3) The optimum control of process (,t) will be searched in the class of the limited piecewisecontinuous functions with the values from the open area Н*. For its construction we use the method of the dynamic programming, according to which the problem is reduced to the minimization of the known functional [1] under the final condition V(tk) = 0 with respect to the optimum functional V = V(,t), parametrically dependent on t  [t0, tk] and determined on the set of functions satisfying (4).
For the processes, described by the linear equations in partial derivative, and criteria of the form of the above-stated ones, functional V is found in the form of the integrated quadratic form [1], therefore in this case we have: whence we have optimum vector hоpt: t) we have the following equation: which is connected with the equation of the APD, having after substitution into it expression оpt h the following form:

Observations suboptimal control
The solution of the obtained equations (6), (7) exhausts completely the problem stated, allowing to generate the required optimum vector -function h of form (3). On the other hand, the solution problem of system (6), (7) is the point-to-point boundary-value problem for integrating the system of the integro-differential equations in partial derivatives, general methods of the exact analytical solution of which , as it is known, does not exist now. Not considering the numerous approximated methods of the solution of the given problem oriented on the trade-off of accuracy against volume of the computing expenses, then as one of the solution methods for this problem we use the method based on the expansion of function v, p in series by some system of the orthonormal functions of the vector argument : , , , , From the point of view of the practical realization the integration of system (8) under the boundary-value conditions appears to be more simple than integration (6), (7), but from the point of view of organization of the estimation process in the real time it is still hindered: first, the volume of the necessary temporary and computing expenses is great, secondly the feasibility of the adjustment of the vector of factors h in the real time of arrival of the signal of measurement Z -is excluded, the prior simulation of realizations Z appears to be necessary (in this case in the course of the instrument realization, as a rule, one fails to maintain the precisely given values h all the same). Thus, the use of the approximated methods of the problem solution (8) is quite proved in this case, then as one of which we consider the method of the invariant imbedding [3], used above and providing the required approximated solution in the real time.
As the application of the given method assumes the specifying of all the components of the required approximately estimated vector in the differential form, then for the realization of the feasibility of the synthesis of vector h through the given method in the real time we introduce a dummy variable v, allowing to take into account from here on expression hopt as the differential equation forming with equations (8) a unified system. The application of the method of the invariant imbedding results in this case in the following system of equations: By virtue of the fact that matrix D in the method of the invariant imbedding plays the role of the weight matrix at the deviation of the vector of the approximated solution from the optimum one, in this case for variables i0 the appropriate components D characterize the degree of their deviation from the factors of expansion of the true APD (components D0 -are deviations of the parameters at the initial moment). The essential advantage of the approach considered, despite the formation of the approximated solution, is the feasibility of the synthesis of the optimum observation function in the real time, i.e. in the course of arrival of the measuring information.

Example
For the illustration of the feasibility of the practical use of the suggested method the numerical simulation of the process of forming vector