In the framework of the Bayesian approach, a suboptimal recursive algorithm for the nonlinear filtering problem of Markov process is proposed. The specificity of this problem is due to the nonlinearity of measurements with a linear nature of the shaping filter for the Markov process being estimated. This algorithm is compared in terms of accuracy, consistency and computational complexity with the algorithm that is close to the optimal one. An illustrative example is given.

Two schemes of suboptimal estimation algorithms synthesized using the Bayesian approach and based on the linearization of functions describing the behavior of the estimated state vector and measurement model are compared for one class of nonlinear estimation problems. One of them is traditional, in which the estimate is found recursively with respect to measurements, and the other one, nonrecursive, involves the simultaneous use of the full set of all available measurements. The advantages and disadvantages of the analyzed algorithms are discussed and illustrated by an example.