A contribution to performance prediction for probabilistic data association tracking filters

Author

Kershaw, D.J. ; Evans, Robin J.

Author_Institution

Aeronaut. & Maritime Res. Lab., DSTO, Melbourne, Vic., Australia

Volume

32

Issue

3

fYear

1996

fDate

7/1/1996 12:00:00 AM

Firstpage

1143

Lastpage

1148

Abstract

The probabilistic data association (PDA) algorithm for tracking in clutter contains a stochastic (data-dependent) Riccati equation for updating the estimation error covariance matrix. This note details a simple analytic approximation to the stochastic Riccati equation that allows precomputation of the estimation error covariance matrices. The potential of the approximation for performance analysis of PDA-based tracking algorithm is demonstrated using a simple example.

Keywords

Kalman filters; clutter; covariance matrices; estimation theory; performance evaluation; probability; stochastic systems; target tracking; tracking filters; analytic approximation; clutter; data-dependent Riccati equation; estimation error covariance matrix; performance analysis; performance prediction; probabilistic data association; stochastic Riccati equation; tracking algorithm; tracking filters; Covariance matrix; Density measurement; Error correction; Estimation error; Filters; Noise measurement; Riccati equations; Stochastic processes; Target tracking; Time measurement; Vectors; Volume measurement;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/7.532274

Filename

532274