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在亚成像阶段,当目标持续释放多组诱饵且间隔极短时,提出了基于区域划分的绝对关联度抗复杂干扰算法。当通过诱饵出现检测算法检测出目标后,首先通过比较目标横、纵坐标突变率判断出目标的突变方向;其次,结合目标模板的长度和高度,根据突变方向对目标和诱饵的粘连区域进行区域划分,其中一块子区域必然包含目标的绝大部分;再次,计算各子区域与目标模板的面积的绝对关联度,通过比较绝对关联度的大小确定目标所在的子区域;最后,根据目标模板的长度和高度,对目标所在子区域进行二次划分,标定出目标。仿真结果表明:基于区域划分的绝对关联度抗复杂干扰算法正确识别率高,算法实时性好。
In the sub-imaging phase, when the target continues to release multiple sets of baits and the interval is very short, an absolute correlation anti-complex interference algorithm based on the region partition is proposed. After detecting the target by the decoy detection algorithm, the direction of the target mutation is first determined by comparing the mutation rate of the target and the vertical axis. Secondly, the region of the adhesion of the target and the decoy is determined according to the length and height of the target template Then, a sub-region of the target must contain most of the target. Third, calculate the absolute degree of association of each sub-region with the target template area, and determine the sub-region where the target is located by comparing the absolute degree of association. Finally, Length and height of the sub-area where the target is divided into secondary, to identify the target. Simulation results show that the algorithm based on region-based absolute correlation anti-complex interference algorithm has a high recognition rate and good real-time performance.