Abstract
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.
Original language | English (US) |
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Title of host publication | 2012 IEEE Radar Conference |
Subtitle of host publication | Ubiquitous Radar, RADARCON 2012 - Conference Program |
Pages | 999-1003 |
Number of pages | 5 |
DOIs | |
State | Published - Jul 30 2012 |
Externally published | Yes |
Event | 2012 IEEE Radar Conference: Ubiquitous Radar, RADARCON 2012 - Atlanta, GA, United States Duration: May 7 2012 → May 11 2012 |
Other
Other | 2012 IEEE Radar Conference: Ubiquitous Radar, RADARCON 2012 |
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Country/Territory | United States |
City | Atlanta, GA |
Period | 5/7/12 → 5/11/12 |
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering