基于检测与跟踪相互迭代的极暗弱目标搜索算法

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。

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

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.