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Stability of the modified probabilistic data association filter: Lyapunov function based analysis

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2 Author(s)
Yong-Shik Kim ; Dept. of Mech. & Intelligent Syst. Eng., Pusan Nat. Univ., South Korea ; Keum-Shik Hong

The probabilistic data association filter (PDAF) is known to provide better tracking performance than the standard Kalman filter in a cluttered environment. In this paper, the stability of the modified PDAF of Fortmann et al. (1985), in the presence of uncertainties with regard to the origin of a measurement, is investigated. The modified Riccati equation derived by approximating two random terms with their expectations is used to prove the stability of the modified PDAF. A new Lyapunov function based approach, which is different from the quantitative evaluation of Li and Bar-Shalom (1991), is pursued. With the assumption that the system and observation noises are bounded, specific tracking error bounds are established

Published in:

Decision and Control, 2000. Proceedings of the 39th IEEE Conference on  (Volume:4 )

Date of Conference:

2000