The multiple target tracking performance of a multiple model technique is investigated. The performance when using a single radar sensor is compared to that when a tandem radar set is employed and the technique implemented is a combination of the interactive multiple model algorithm and the probabilistic data association filter. The investigation shows that the inclusion of a second redundant radar sensor significantly improves the tracking performance of the multiple model technique with only a slight increase in the computation cost. The ability to initiate, maintain, and terminate the tracks of multiple targets in a cluttered adaptive cruise control environment is improved a) by reducing the chances of wrongfully terminating tracks when targets are not "picked up" during a small number of consecutive radar scans and b) by reducing the state estimate covariances. A method for showing this latter improvement involves calculating the spectrum of the relative covariance matrix. These benefits of the redundant sensor counter those of a complementary sensor where a greater field of view is afforded.
[1]
Y. Bar-Shalom.
Tracking and data association
,
1988
.
[2]
Y. Bar-Shalom,et al.
Topography-based VS-IMM estimator for large-scale ground target tracking
,
1999
.
[3]
Paul J.Th. Venhovens,et al.
Stop and Go Cruise Control
,
2000
.
[4]
Yaakov Bar-Shalom,et al.
Design of an interacting multiple model algorithm for air traffic control tracking
,
1993,
IEEE Trans. Control. Syst. Technol..
[5]
Y. Bar-Shalom,et al.
Tracking in a cluttered environment with probabilistic data association
,
1975,
Autom..
[6]
R. Bishop,et al.
A survey of intelligent vehicle applications worldwide
,
2000,
Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).
[7]
Paul Levi,et al.
Advanced lane recognition-fusing vision and radar
,
2000,
Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).