摘要
针对防空雷达网对多隐身目标检测与跟踪时雷达分配问题,该文将二值粒子群优化(BPSO)用于雷达分配,结合粒子滤波,提出了一种隐身目标的协同检测与跟踪算法。该算法将雷达分配问题转化成组合优化问题,根据目标的隐身特性设计雷达分配方案(RAS),借助随机分布的检测粒子计算不同RAS对新生目标的检测概率,同时根据RAS对已跟踪目标位置的后验克拉美罗界衡量跟踪精度,采用BPSO算法在RAS中进行全局搜索,选择最优分配方案进行粒子滤波与融合跟踪。与现有算法相比,该算法不仅能够及时检测新生目标,而且能够利用组网优势持续且优化跟踪隐身目标,使网络的整体跟踪精度得到显著提高,实现多目标协同跟踪。
Focusing on the radar allocation for stealth targets detection and tracking issue in air-defense radar network, a novel collaborative detection and tracking algorithm that combines Binary Particle Swarm Optimization （BPSO） and particle filtering to cope with the radar allocation is proposed in this paper. In the proposed algorithm, Radar Allocation Schemes （RAS） are designed according to the characters of stealthy targets, and the particles distributed randomly are applied to obtain the detection probability of newborn targets. Then the tracking accuracy is measured by the Posterior Cram^r-Rao Lower Bound （PCRLB） of the tracked targets. Moreover, the BPSO is selected to search the whole RAS, and the results of particle filtering of the selected tracking radars are fused. Simulation results show that the proposed method can not only quickly identify newborn targets, but also optimize the tracking performance of the existing targets, and improve the tracking accuracy of the whole radar network compared with traditional methods.
出处
《电子与信息学报》
EI
CSCD
北大核心
2013年第3期601-607,共7页
Journal of Electronics & Information Technology
基金
长江学者和创新团队发展计划(IRT0645)
中央高校基本科研业务费专项资金(K5051202036)资助课题
关键词
防空雷达网
协同检测与跟踪
二值粒子群优化
后验克拉美罗界
Air-defense radar network
Collaborative detection and tracking
Binary Particle Swarm Optimization（BPSO）
Posterior Cram^r-Rao Lower Bound （PCRLB）
