Bernouli Forward-Backward Smoothing for Track-Before-Detect
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Track-before-detect (TBD) refers to an alternative approach to tracking which utilizes the full sensor information rather than detections obtained from thresholding. In this letter we investigate whether forward-backward smoothing for TBD can increase performance. We propose a novel algorithm based on the random finite set framework which incorporates the TBD sensor model with multi-scan information. The algorithm is tested on a typical scenario which confirms improved tracking.
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