针对深空慢速运动目标(目标在焦面的运动速度小于1 pixel/frame)提出了一种最大似然条痕检测算法,能够在较低信噪比情况下实现有效的慢速目标检测。算法将目标脉冲形状信息引入信号模型中,是最大值投影算法的改进形式。建立基于高斯噪声分布的图像信号模型,在此基础上推导了最大似然条痕检测算法模型;分析该算法的实时性及其理论探测性能;采用蒙特卡罗仿真方法比较最大似然条痕检测算法与最大值投影检测算法的检测性能。仿真结果表明,输入信噪比为3.5时,最大似然条痕检测算法的探测概率为95%,其相同探测概率条件下所需信噪比比最大值投影算法降低了2.5(即最大值投影算法要达到95%的检测概率,所需信噪比为6)。算法实时性分析表明,最大似然条痕检测算法的实时处理能力为31.25 Mb/s。

A maximum likelihood streak detection (MLSD) algorithm is presented for the slow moving target(target velocity in the focal plane array is smaller than 1 pixel per frame)in deep space,it can implement the detection of slow target under the low Signal to Noise Ratio (SNR) condition effectively. The target pulse shape information is included in the signal model,and it is an improved version of the Maximum Value Projection Detection (MVPD) algorithm. The image signal mathematics model is built based on gaussian noise distribution,Its theory detection performance model and the real-time performance are analyzed. Monte Carlo simulation technique is adopted in comparing and analyzing the detection performance of MLSD and MVPD algorithm using simulated sequence images. The result shows that the detection probability of the MLSD reaches to 95 percent when the input SNR is 3.5,and the input SNR required for the MLSD is 2.5 lower than MVPD under the same detection probability. (required an input SNR of 6 for the MVPD). The real-time processing capability of the MLSD can obtain 31.25 Mb/s.