Lyapunov stability of a tracking filter with the uncertainty of measurement origin
Yong-Shik Kim, Keum‐Shik Hong · 2002
The probabilistic data association filter (PDAF) is known to provide better tracking performance than the standard Kalman filter (KF) in a cluttered environment. In this paper, the stability of the PDAF of Fortmann et al. (1985), in the presence of uncertainties with regard to the origin of measurement, is investigated. The modified Riccati equation derived by approximating two random terms with their expectations is used to evaluate the stability of the 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.