Approximate linear minimum variance filters for continuous-discrete state space models: convergence and practical algorithms
Provides a theoretically grounded approximation method for nonlinear filtering in continuous-discrete systems, relevant for practitioners dealing with sparse, noisy observations.
The paper introduces approximate linear minimum variance filters for continuous-discrete state space models, proving convergence to the exact filter under decreasing approximation error. Practical algorithms are provided and demonstrated on simulation examples.
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-$β$ 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.