多站雷达干扰对抗系统子站选择策略

A Subset Selection Strategy on Multiple-Radar Anti-Jamming Systems

基于参数联合估计的假目标鉴别方法可通过增加雷达站数量来提高假目标鉴别概率,然而,过度增加雷达站数量会造成设备资源的严重浪费.对此,提出基于多站雷达系统假目标鉴别过程中渐进收缩的子站选择策略.对于空间已有的雷达站,在满足预设假目标鉴别性能的前提下,考虑通过快速收缩和全局收缩2种筛选方式,迭代选出系统中空间分布更有优势、鉴别能力更强的发射或接收站,共同组成雷达子站.相比于穷举搜索方法,子站选择策略可大幅降低筛选过程的时间复杂度.仿真结果表明,子站能够保持与原多站雷达系统近似的鉴别效果,同时优化了雷达设备数量,减少了融合中心处理的数据量和所需的通信链路,有效节约了运作成本.

For the false target identification method based on joint estimation of parameters,the discrimination of false target probability can be improved by increasing the number of radar stations.However,the excessive increased radar stations will cause a serious waste of equipment resources.For this problem,a gradual shrinkage subset selection strategy on multiple-radar anti-jamming systems is proposed.Aiming to existing radar stations,the rapid shrinkage method and the global shrinkage method are considered to select some transmitting and receiving stations to form the radar subset which guarantee the preset false target discrimination performance.All of the selecting stations have better spatial distribution or stronger discrimination ability in the system.Compared with exhaustive search,the proposed subset selection strategy has a great reduction in computational complexity.Simulation shows that the radar subset can maintain the similar discrimination performance with the original multiple-radar systems.At the same time,it optimizes the number of radar stations,reduces the amount of data processed by the fusion center and the required communication links,which effectively save the operating cost.