Approximate linear minimum variance filters for continuous-discrete state space models: convergence and practical algorithms

TL;DR

This paper introduces approximate linear minimum variance filters for continuous-discrete state space models, demonstrating their convergence and providing practical algorithms for nonlinear stochastic system identification from sparse, noisy data.

Contribution

It presents a new recursive approximation method for LMV filters, including detailed local linearization filters and practical algorithms with demonstrated performance.

Findings

Filters converge as prediction errors decrease

Order-β local linearization filters are effective

Algorithms perform well in simulation examples

Abstract

In this paper, approximate Linear Minimum Variance (LMV) filters for continuous-discrete state space models are introduced. The filters are obtained by means of a recursive approximation to the predictions for the first two moments of the state equation. It is shown that the approximate filters converge to the exact LMV filter when the error between the predictions and their approximations decreases. As particular instance, the order-$\beta$ Local Linearization filters are presented and expounded in detail. Practical algorithms are also provided and their performance in simulation is illustrated with various examples. The proposed filters are intended for the recurrent practical situation where a nonlinear stochastic system should be identified from a reduced number of partial and noisy observations distant in time.