Approximations to optimal nonlinear filters

Harold J. Kushner

IEEE Transactions on Automatic Control · 1967 · 314 citations · 19 references

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TL;DR

The study concerns nonlinear stochastic differential equations where the optimal filter is typically infinite dimensional and linearization methods fail due to large noise and nonlinear effects. The authors aim to explore finite‑dimensional approximations to the optimal nonlinear filter and discuss the associated simulation challenges. They evaluate multiple finite‑dimensional approximation techniques and analyze simulation issues. Numerical experiments reveal useful properties of the approximating filters and point to potential improvements.

Abstract

Let the signal and noise processes be given as solutions to nonlinear stochastic differential equations. The optimal filter for the problem, derived elsewhere, is usually infinite dimensional. Several methods of obtaining possibly useful finite dimensional approximations are considered here, and some of the special problems of simulation are discussed. The numerical results indicate a number of useful features of the approximating filters and suggest methods of improvement. The paper is concerned with problems where the noise and nonlinear effects are much too large for the use of "linearization" methods, which for the simulated problem, at least, were useless.

References

19