Combined particle and smooth innovation filtering for nonlinear estimation

W. Hilal; S. A. Gadsden; S. A. Wilkerson; M. AlShabi

doi:10.1117/12.2618973

8 June 2022 Combined particle and smooth innovation filtering for nonlinear estimation

W. Hilal, S. A. Gadsden, S. A. Wilkerson, M. AlShabi

Proceedings Volume 12122, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI; 1212204 (2022) https://doi.org/10.1117/12.2618973
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

In this paper, a new state and parameter estimation method is introduced based on the particle filter (PF) and the sliding innovation filter (SIF). The PF is a popular estimation method, which makes use of distributed point masses to form an approximation of the probability distribution function (PDF). The SIF is a relatively new estimation strategy based on sliding mode concepts, formulated in a predictor-corrector format. It has been shown to be very robust to modeling errors and uncertainties. The combined method (PF-SIF) utilizes the estimates and state error covariance of the SIF to formulate the proposal distribution which generates the particles used by the PF. The PF-SIF method is applied on a nonlinear target tracking problem, where the results are compared with other popular estimation methods.

Conference Presentation

Citation Download Citation

W. Hilal, S. A. Gadsden, S. A. Wilkerson, and M. AlShabi "Combined particle and smooth innovation filtering for nonlinear estimation", Proc. SPIE 12122, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI, 1212204 (8 June 2022); https://doi.org/10.1117/12.2618973

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