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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 is
intended to indicate the uncertainty or inaccuracy of the target 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 and the estimation filter; consequently, degraded track consistency might cause misassociations
(correlation errors) and degraded filter processing that can 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 accuracy of each target track.
In the development of target trackers, 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 covariance compensation to reduce the
degradation of consistence due to potential misassociations in measurement fusion using single-frame data association.
This compensation approach used is also applicable to other fusion approaches and to tracking with data from a single
sensor. This paper also shows how this compensation approach can be applied to a variety of data association
algorithms.
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