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Tracking performance in surveillance systems depends on two interrelated functions: track updating, the process of incorporating a new measurement into the track to update the system state estimate, and return-to-track correlation, the process of selecting which sensor return, if any, to use for track updating. Because of the presence of a number of targets in the same vicinity and the existence of clutter and false alarms, the correlation function is generally performed imperfectly. Since typical tracking filters such as the Kalman filter do not account for such correlation errors, degraded performance often results as well as unreliable and optimistic estimates of tracking accuracies. This paper examines and provides for optimizing the overall tracking process considering both the correlation and track update functions and their interaction. General equations for tracking performance of any arbitrary tracking filter used with a broad class of correlation algorithms in dense multitarget environments are developed. A new reoptimized tracking filter is derived which provides, from among a general class of tracking filters using a priori information on sensor return statistics, optimal performance in such environments and which reduces to the Kalman filter when environmental effects are eliminated. The new filter is compared parametrically to both the standard Kalman filter and a computationally simpler version of the optimal filter in terms of tracking accuracy and reliability of the calculated covariance matrix, over a spectrum of environmental conditions. At high densities of sensor returns, the new filter provides considerably improved tracking performance as well as uniquely reliable estimates of this performance.