Target Detection and Tracking With Errors in Intensity and Mapping Dimensions

Abstract

This paper addresses an engineering problem that measurement errors in intensity and mapping dimensions exist in the track-before-detect (TBD) architecture simultaneously. Drawing lessons from the probability data association (PDA) and the basic generalized likelihood ratio test TBD (GLRT-TBD), the joint log-likelihood ratio test TBD (JLLRT-TBD) method is proposed. Unlike conventional TBD algorithms, which consider intensities captured in the cells occupied by physically admissible transitions only, the novel method regards the cells centered at the transitions as effective ones. After that, detection probabilities are deduced from their positions, and then weighting likelihood ratios of their intensities results in metrics of different transitions. Finally, the maximum metric is compared with the designed threshold, and the estimation of the target state is obtained by retracing the maximization process. With this, the incoherent integration gain along consecutive scans is acquired, and the increment in computational complexity is avoided. Monte Carlo numerical results demonstrate that the detection and estimation performances of the JLLRT-TBD are superior significantly to those of the basic GLRT-TBD while the lower averaged execution time is spent.