Use of joint data association probabilities for covariance consistency

W. Dale Blair; George C. Brown

doi:10.1117/12.492743

26 November 2001 Use of joint data association probabilities for covariance consistency

W. Dale Blair, George C. Brown

Proceedings Volume 4473, Signal and Data Processing of Small Targets 2001; (2001) https://doi.org/10.1117/12.492743
Event: International Symposium on Optical Science and Technology, 2001, San Diego, CA, United States

Abstract

Covariance consistency is a critical element of a robust target tracking system. Target maneuvers and measurement origin uncertainty pose significant challenges to a tracking algorithm achieving covariance consistency. The Interacting Multiple Model (IMM) estimator is a nearly consistent estimator for tracking maneuvering targets. While the Probabilistic Data Association Filter (PDAF) achieves covariance consistency for a single target in presence of false alarms, achieving covariance consistency while tracking multiple closely-spaced targets is an open presence of false alarms, achieving covariance consistency while tracking multiple closely-spaced targets is an open issue. When using an unique assignment technique for associating measurements-to-track association probabilities are unity for each measurement-track pair. This processing of the measurements results in poor covariance consistency for closely-spaced targets. In this paper, the use of approximate association probabilities for each measurement-to-track pair is proposed for the unique assignments and included in the track filter processing of the measurement to enhance the covariance consistency for closely-spaced targets.

Citation Download Citation

W. Dale Blair and George C. Brown "Use of joint data association probabilities for covariance consistency", Proc. SPIE 4473, Signal and Data Processing of Small Targets 2001, (26 November 2001); https://doi.org/10.1117/12.492743

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