Huo Weibo, Pei Jifang, Huang Yulin, Zhang Qian, Yang Jianyu
School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel). 2019 Apr 2;19(7):1586. doi: 10.3390/s19071586.
Maritime moving target detection and tracking through particle filter based track-before-detect (PF-TBD) has significant practical value for airborne forward-looking scanning radar. However, villainous weather and surging of ocean waves make it extremely difficult to accurately obtain a statistical model for sea clutter. As the likelihood ratio calculation in PF-TBD is dependent on the distribution of the clutter, the performance of traditional distribution-based PF-TBD seriously declines. To resolve these difficulties, this paper proposes a new target detection and tracking method, named spectral-residual-binary-entropy-based PF-TBD (SRBE-PF-TBD), which is independent from the prior knowledge of sea clutter. In the proposed method, the likelihood ratio calculation is implemented by first extracting the spectral residual of the input image to obtain the saliency map, and then constructing likelihood ratio through a binarization processing and information entropy calculation. Simulation results show that the proposed method had superior performance of maritime moving target detection and tracking.
通过基于粒子滤波器的检测前跟踪（PF-TBD）实现海上移动目标检测与跟踪，对机载前视扫描雷达具有重要的实用价值。然而，恶劣天气和海浪涌动使得准确获取海杂波统计模型极为困难。由于PF-TBD中的似然比计算依赖于杂波分布，传统基于分布的PF-TBD性能严重下降。为解决这些难题，本文提出一种新的目标检测与跟踪方法，即基于谱残差-二元熵的PF-TBD（SRBE-PF-TBD），该方法独立于海杂波先验知识。在所提方法中，似然比计算首先通过提取输入图像的谱残差获得显著图，然后通过二值化处理和信息熵计算构建似然比。仿真结果表明，所提方法在海上移动目标检测与跟踪方面具有优越性能。
