Applications of MHT to dim moving targets

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Abstract
This paper discusses the application of a particular implementation of Multiple Hypothesis Tracking (MHT) to the problem of detection and tracking of dim targets in a heavy clutter or false alarm background. The MHT method and the performance improvement associated with MHT for these applications is well documented [1-6], but the actual implementation has been limited due to the computational load and complexity associated with "traditional" implementations. We present an approach (Structured Branching) that offers significant computational savings as compared with alternative approaches, and can maintain hundreds of "possible" tracks that are initiated in a dense clutter or false alarm background without overwhelming computational or memory requirements. Further, this method can be applied to much more limited implementations according to the computational resources available-there is minimal "overhead" associated with Structured Branching (SB) since hypotheses are not propagated explicitly. The SB algorithm is described, highlighting the ways in which computational savings are achieved, and simulation results are presented. Then, approximate techniques are developed for predicting the performance of MHT (any implementation, not just SB), and results comparing predicted performance with simulation results are presented.

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