Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (10): 3017-3024.DOI: 10.11772/j.issn.1001-9081.2020122000

Special Issue: 多媒体计算与计算机仿真

• Multimedia computing and computer simulation • Previous Articles     Next Articles

Extremely dim target search algorithm based on detection and tracking mutual iteration

XIAO Qi1,2,3, YIN Zengshan1,2,3, GAO Shuang1   

  1. 1. Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201203, China;
    2. School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China;
    3. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-12-18 Revised:2021-04-21 Online:2021-10-10 Published:2021-10-27
  • Supported by:
    This work is partially supported by the Key Deployment Project of National Defense Science and Technology Innovation of Chinese Academy of Sciences (KGFZD-135-20-03).


効琦1,2,3, 尹增山1,2,3, 高爽1   

  1. 1. 中国科学院微小卫星创新研究院, 上海 201203;
    2. 上海科技大学 信息科学与技术学院, 上海 201210;
    3. 中国科学院大学, 北京 100049
  • 通讯作者: 尹增山
  • 作者简介:効琦(1995-),男,甘肃定西人,硕士研究生,主要研究方向:遥感图像处理、视频目标检测;尹增山(1971-),男,山东平度人,研究员,博士,主要研究方向:图像信息处理、遥感技术、卫星网络;高爽(1986-),女,山东青岛人,副研究员,博士,主要研究方向:遥感图像处理、微纳卫星系统。
  • 基金资助:

Abstract: It is difficult to distinguish the intensity between dim moving targets and background noise in the case of extremely Low Signal-to-Noise Ratio (LSNR). In order to solve the problem, a new extremely dim target search algorithm based on detection and tracking mutual iteration was proposed with a new strategy for combining and iterating the process of temporal domain detection and spatial domain tracking. Firstly, the difference between the signal segment in the detection window and the extracted background estimated feature was calculated during the detection process. Then, the dynamic programming algorithm was adopted to remain the trajectories with the largest trajectory energy accumulation in the tracking process. Finally, the threshold parameters of the detector of the remained trajectory were adaptively adjusted in the next detection process, so that the pixels in this trajectory were able to be retained to the next detection and tracking stage with a more tolerant strategy. Experimental results show that, the dim moving targets with SNR as low as 0 dB can be detected by the proposed algorithm, false alarm rate of 1% - 2% and detection rate of about 70%. It can be seen that the detection ability for dim targets with extremely LSNR can be improved effectively by the proposed algorithm.

Key words: dim small target, wavelet packet, kernel function, spatio-temporal domain combination, detection and tracking iteration, dynamic programing

摘要: 针对极低信噪比(LSNR)情况下暗弱运动目标和背景噪声的强度难以区分的问题,提出了一种基于检测与跟踪相互迭代的极暗弱目标搜索算法,总体上采用将时域检测与空域跟踪的过程联合、迭代进行的新型策略。首先,在检测过程中计算检测窗口内信号片段与已经提取的背景估计特征的差别;然后,在跟踪过程中运用动态规划算法保留使得轨迹能量累积最大的轨迹;最后,自适应地调整下一检测过程中被保留轨迹的检测器阈值参数,使该轨迹内的像素能以更宽容的策略被保留到下一检测跟踪阶段。实验测试结果表明,所提算法可以在1%~2%的虚警率和约70%的检测率下探测到低至0 dB的暗弱运动目标。可见该算法可有效改善对LSNR暗弱目标的检测能力。

关键词: 暗弱小目标, 小波包, 核函数, 时空域联合, 检测与跟踪迭代, 动态规划

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