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给出了Jeffrey H.Shapiro等人的目标检测算法,以及在该算法基础上推导出的七个目标检测公式。自行编程设计和实现了仿真实验。在理想情况下,通过仿真对检测性能进行了对比,从仿真结果可知,!的取值在5 dB附近或者大于5 dB时,在激光雷达的工作范围内(CNR≈10￣30 dB),综合利用主被动两种信息进行目标检测的性能要优于仅利用其中任何一种信息进行目标检测的性能,综合利用主被动三种信息进行目标检测的性能要优于仅利用其中任何一种信息以及两两信息组合进行目标检测的性能。强度像、距离像与被动红外融合时的检测性能最高,从而验证了长波红外主被动复合成像的优越性。同时,讨论了理想情况下子帧大小对目标检测公式性能的影响,对非理想情况进行了初步探讨。
The target detection algorithm of Jeffrey H. Shapiro et al. And the seven target detection formulas derived from the algorithm are given. Self-programming design and implementation of simulation experiments. Under ideal conditions, the performance of the detection is compared by simulation. From the simulation results, it can be seen from the simulation results that in the working range of the lidar (CNR≈10 ~ 30 dB) when the value of δ is around 5 dB or greater than 5 dB, The performance of target detection using both active and passive information is better than the performance of using only one of the two kinds of information to perform target detection. The comprehensive utilization of active and passive three kinds of information to perform target detection is better than using only either of them. The performance of target detection for each combination of two information. Intensity, distance and the fusion of passive infrared detection performance when the highest, which proves the long-wave infrared active-passive composite imaging superiority. At the same time, the influence of the sub-frame size on the performance of the target detection formula under ideal conditions is discussed, and the non-ideal case is discussed.