TY - GEN
T1 - Optimal tracking index relationship for random and deterministic target maneuvers
AU - Urbano, Leonardo F.
AU - Kalata, Paul
AU - Kam, Moshe
PY - 2012
Y1 - 2012
N2 - In the standard formulation of the Kalman filter, target maneuver (acceleration) is assumed to be a random process that can be modeled as zero-mean additive white noise in the filter plant model. In the optimal reduced state estimator (ORSE) recently introduced by Mookerjee and Reifler, target maneuver is assumed to be a deterministic parameter in the plant model, equal to the maximum target acceleration. In this paper we exploit the steady-state equivalency of the Kalman filter and ORSE to derive an exact analytic expression relating the random tracking index of the Kalman filter, Λ R, and the deterministic tracking index of the ORSE, Λ D. The relationship offers a solution to a central problem in target tracking theory, namely how should the white plant noise level for a Kalman filter be selected for minimum mean square error state estimates in the presence of maximum target acceleration? Using the new relationship, a Kalman filter can be constructed with identical steady-state performance to the ORSE but without the additional computational complexity of the ORSE.
AB - In the standard formulation of the Kalman filter, target maneuver (acceleration) is assumed to be a random process that can be modeled as zero-mean additive white noise in the filter plant model. In the optimal reduced state estimator (ORSE) recently introduced by Mookerjee and Reifler, target maneuver is assumed to be a deterministic parameter in the plant model, equal to the maximum target acceleration. In this paper we exploit the steady-state equivalency of the Kalman filter and ORSE to derive an exact analytic expression relating the random tracking index of the Kalman filter, Λ R, and the deterministic tracking index of the ORSE, Λ D. The relationship offers a solution to a central problem in target tracking theory, namely how should the white plant noise level for a Kalman filter be selected for minimum mean square error state estimates in the presence of maximum target acceleration? Using the new relationship, a Kalman filter can be constructed with identical steady-state performance to the ORSE but without the additional computational complexity of the ORSE.
UR - http://www.scopus.com/inward/record.url?scp=84864237476&partnerID=8YFLogxK
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U2 - 10.1109/RADAR.2012.6212283
DO - 10.1109/RADAR.2012.6212283
M3 - Conference contribution
AN - SCOPUS:84864237476
SN - 9781467306584
T3 - IEEE National Radar Conference - Proceedings
SP - 999
EP - 1003
BT - 2012 IEEE Radar Conference
T2 - 2012 IEEE Radar Conference: Ubiquitous Radar, RADARCON 2012
Y2 - 7 May 2012 through 11 May 2012
ER -