Paper
19 May 2006 On target track covariance consistency
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Abstract
The primary components of a target track are the estimated state vector and its error variance-covariance matrix (or simply the covariance). The estimated state indicates the location and motion of the target. The track covariance should indicate the uncertainty or inaccuracy of the state estimate. The covariance is computed by the track processor and may or may not realistically indicate the inaccuracy of the state estimate. Covariance Consistency is the property that a computed variance-covariance matrix realistically represents the covariance of the actual errors of the estimate. The computed covariance of the state estimation error is used in the computations of the data association processing function; consequently, degraded track consistency causes misassociations (correlation errors) that can substantially degrade track performance. The computed covariance of the state estimation error is also used by downstream functions, such as the network-level resource management functions, to indicate the accuracy of the target state estimate. Hence, degraded track consistency can mislead those functions and the war fighter about how accurate each target track is. In the past, far more attention has been given to improving the accuracy of the estimated target state than in improving the track covariance consistency. This paper addresses the importance and analyzes properties of covariance consistency. Monte Carlo simulation results illustrate the characteristics of covariance consistency and the performance with some simple methods for improving covariance consistency.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Oliver E. Drummond, Albert J. Perrella Jr., and Steven Waugh "On target track covariance consistency", Proc. SPIE 6236, Signal and Data Processing of Small Targets 2006, 623615 (19 May 2006); https://doi.org/10.1117/12.673201
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CITATIONS
Cited by 14 scholarly publications.
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KEYWORDS
Error analysis

Mahalanobis distance

Monte Carlo methods

Matrices

Data fusion

Digital filtering

Sensors

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