Comparison of recursive and nonrecursive linearization-based algorithms for one class of nonlinear estimation problems*

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.