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Proceedings Paper

The small low SNR target tracking using sparse representation information
Author(s): Lifan Yin; Yiqun Zhang; Shuo Wang; Chenggang Sun
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Paper Abstract

Tracking small targets, such as missile warheads, from a remote distance is a difficult task since the targets are “points” which are similar to sensor’s noise points. As a result, traditional tracking algorithms only use the information contained in point measurement, such as the position information and intensity information, as characteristics to identify targets from noise points. But in fact, as a result of the diffusion of photon, any small target is not a point in the focal plane array and it occupies an area which is larger than one sensor cell. So, if we can take the geometry characteristic into account as a new dimension of information, it will be of helpful in distinguishing targets from noise points. In this paper, we use a novel method named sparse representation (SR) to depict the geometry information of target intensity and define it as the SR information of target. Modeling the intensity spread and solving its SR coefficients, the SR information is represented by establishing its likelihood function. Further, the SR information likelihood is incorporated in the conventional Probability Hypothesis Density (PHD) filter algorithm with point measurement. To illustrate the different performances of algorithm with or without the SR information, the detection capability and estimation error have been compared through simulation. Results demonstrate the proposed method has higher estimation accuracy and probability of detecting target than the conventional algorithm without the SR information.

Paper Details

Date Published: 15 November 2017
PDF: 8 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060523 (15 November 2017); doi: 10.1117/12.2292658
Show Author Affiliations
Lifan Yin, The Second Academy of CASIC (China)
Yiqun Zhang, Beijing Institute of Electronic System Engineering (China)
Shuo Wang, Beijing Institute of Electronic System Engineering (China)
Chenggang Sun, Beijing Institute of Electronic System Engineering (China)

Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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