1 September 1995 Clutter mitigation in Bayesian field tracking
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In this paper we address the problem of tracking low-SNR targets in an environment of heavy, stationary clutter (e.g., ground clutter). The processing approach is Bayesian field tracking, in which a posterior target distribution is developed over the entire position-velocity state space. Development of the Bayesian posterior probabilities is recursive and is driven by likelihood fields evaluated from successive measurement observations. This track-before-detect approach has a demonstrated capability to track at SNR levels below those for which the usual Kalman- based tracker is functional. We have applied two clutter mitigation techniques that correspond individually to the following clutter characteristics: (1) the occurrence of high-amplitude (non- Gaussian) events and (2) the stationary nature of clutter features. The first characteristic is treated by a composite Gaussian environment model and the second by active discrimination against features tracked (by the Bayesian field tracker) at zero velocity. Both mitigation techniques combine to provide a decision theory test of signal versus noise or clutter, and the net impact is rigorously integrated into the Bayesian tracker processing through the likelihood- ratio field that drives the recursive update. Evaluation of ROC curves for simulated and real environments show both mitigation techniques to be highly effective.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert G. Lindgren, Robert G. Lindgren, Lisa A. Taylor, Lisa A. Taylor, Henry H. Suzukawa, Henry H. Suzukawa, } "Clutter mitigation in Bayesian field tracking", Proc. SPIE 2561, Signal and Data Processing of Small Targets 1995, (1 September 1995); doi: 10.1117/12.217673; https://doi.org/10.1117/12.217673


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