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

Author

Kim, Yong-Shik ; Hong, Keum-Shik

Author_Institution

Dept. of Mech. & Intelligent Syst. Eng., Pusan Nat. Univ., South Korea

Volume

4

fYear

2000

fDate

2000

Firstpage

3896

Abstract

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

Keywords

Lyapunov methods; Riccati equations; filtering theory; probability; stability; state estimation; target tracking; Lyapunov function; Riccati equation; probabilistic data association filter; stability; state estimation; target tracking; Convergence; Covariance matrix; Filters; Lyapunov method; Object detection; Performance analysis; Riccati equations; Stability analysis; Steady-state; Target tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2000. Proceedings of the 39th IEEE Conference on

Conference_Location

Sydney, NSW

ISSN

0191-2216

Print_ISBN

0-7803-6638-7

Type

conf

DOI

10.1109/CDC.2000.912321

Filename

912321