Derivation of the sliding innovation information filter for target tracking

Naseem Alsadi; Waleed Hilal; S. Andrew Gadsden; Mohammad Al-Shabi

doi:10.1117/12.2663909

14 June 2023 Derivation of the sliding innovation information filter for target tracking

Naseem Alsadi, Waleed Hilal, S. Andrew Gadsden, Mohammad Al-Shabi

Proceedings Volume 12547, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII; 1254708 (2023) https://doi.org/10.1117/12.2663909
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

An information filter is one that propagates the inverse of the state error covariance, which is used in the state and parameter estimation process. The term ‘information’ is based on the Cramer-Rao lower bound (CRLB), which states that the mean square error of an estimator cannot be smaller than an amount based on its corresponding likelihood function. The most common information filter (IF) is derived based on the inverse of the Kalman filter (KF) covariance. This paper introduces preliminary work completed on developing the information form of the sliding innovation filter. The SIF is a relatively new type of predictor-corrector estimator based on sliding mode concepts. In this brief paper, the recursive equations used in the sliding innovation information filter (SIIF) are derived and summarized. Preliminary results of application to a target tracking problem are also studied.

Conference Presentation

Citation Download Citation

Naseem Alsadi, Waleed Hilal, S. Andrew Gadsden, and Mohammad Al-Shabi "Derivation of the sliding innovation information filter for target tracking", Proc. SPIE 12547, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII, 1254708 (14 June 2023); https://doi.org/10.1117/12.2663909

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