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针对雷达组网对隐身目标协同检测与跟踪时的动态分配问题,将条件后验克拉美罗下界(CPCRLB)用作系统跟踪性能的度量,结合改进二值粒子群优化(NBPSO)和粒子滤波,提出了一种基于CPCRLB的隐身目标协同检测与跟踪算法。该算法将雷达的动态分配问题转化成组合优化问题,根据新生目标的隐身特性对雷达分配方案的约束,借助分布在边界的检测粒子计算不同的雷达分配方案对新生目标的检测概率,并以已跟踪目标的CPCRLB衡量跟踪精度,采用NBPSO全局搜索最优分配方案,最后进行粒子滤波与协方差交集融合。
Aiming at the problem of dynamic allocation of cooperative detection and tracking of stealth targets in radar networks, the conditional post-clarithromic lower bound (CPCRLB) is used as a measure of system tracking performance. Combined with improved binary particle swarm optimization (NBPSO) and particle filter, A covert detection and tracking algorithm based on CPCRLB is proposed. The algorithm transforms the dynamic assignment of radar into a combinatorial optimization problem. According to the stealth characteristics of the new target, the detection probability of the new target is calculated by the detection particles distributed on the boundary according to the stealth characteristics of the new target. Tracking accuracy of tracking target CPCRLB, using NBPSO global search optimal distribution scheme, and finally the particle filter and covariance intersection fusion.