5 January 2004 Tracking targets using matched field observations
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Abstract
The target tracking literature has traditionally been most interested in the "hit" model for the observations process, and the community has developed many techniques for data association. On the other hand, matched field processing (MFP) research has focused on signal processing with the main emphasis on target detection and localization. Treatments of combined tracking/MFP systems are not common, but most concentrate on signal processing, with the idea that a "track" is really a sequence or track-segment of detections that make sense from dynamics considerations. Thus, here we explore the MFP tracking problem, with the key that we attempt to use traditional target-tracking algorithms. In particular, we use an IMMPDAF-AI (interacting multiple-model probabilistic data association filter with amplitude information). It is shown that the use of such an advanced tracking algorithm – plus a number of MFP-specific refinements – produces tracking performance that is far superior to that obtained for a more traditional tracking (a strongest-neighbor Kalman filter), with the added advantage of a significantly reduced numerical load as measured in terms of the number of MFP replicas to be computed.
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Peter K. Willett, Peter K. Willett, Judith Bishop, Judith Bishop, Evangelos Giannopoulos, Evangelos Giannopoulos, } "Tracking targets using matched field observations", Proc. SPIE 5204, Signal and Data Processing of Small Targets 2003, (5 January 2004); doi: 10.1117/12.504879; https://doi.org/10.1117/12.504879
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