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。

To solve the problem of radar allocation for stealth targets detection and tracking in radar network, a collaborative detection and tracking algorithm for stealth targets based on conditional posterior Cramér-rao lower bound (CPCRLB) is proposed in this paper. In the proposed algorithm, radar allocation schemes are constrained by the stealthy characters of newborn targets, and the particles distributed at the boundary region are applied to obtain the detection probability of newborn targets. The tracking accuracy is measured by the CPCRLB of the tracked targets. The novel binary particle swarm optimization (NBPSO) is selected to search the global optimal radar allocation scheme. Then the results of particle filtering of the selected tracking radars are fused by covariance intersection algorithm.

TN953

国家自然科学基金资助项目