The small low SNR target tracking using sparse representation information

Lifan Yin; Yiqun Zhang; Shuo Wang; Chenggang Sun

doi:10.1117/12.2292658

15 November 2017 The small low SNR target tracking using sparse representation information

Lifan Yin, Yiqun Zhang, Shuo Wang, Chenggang Sun

Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 1060523 (2017) https://doi.org/10.1117/12.2292658
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China

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.

Citation Download Citation

Lifan Yin, Yiqun Zhang, Shuo Wang, and Chenggang Sun "The small low SNR target tracking using sparse representation information", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060523 (15 November 2017); https://doi.org/10.1117/12.2292658

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