Title :
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
fDate :
7/1/1996 12:00:00 AM
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;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
