1 September 1995 Clutter mitigation in Bayesian field tracking
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Abstract
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.
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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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