Title
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.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2927532
IEEE ACCESS
Keywords
DocType
Volume
Measurement error, mapping dimensions, GLRT-TBD, JLLRT-TBD
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Zhen Luo100.34
Jun Wang29228736.82
Yaqi Deng301.01
Yun Zhu4629.64
Hengzhou Xu51212.24