摘要
基于粒子滤波的检测前跟踪方法是解决空空导弹弱小信号检测跟踪问题的重要手段,然而常规的PF-TBD算法中,普遍存在着新生粒子与持续粒子的比例选取问题,其直接影响着算法的收敛速度及跟踪精度。针对这一问题,本文提出一种自适应的粒子比优化方法,利用前一时刻检测概率对粒子比进行优化设计,相比于传统的固定粒子比方法,其能根据历史检测信息对粒子比进行自适应调整。仿真结果表明,本文提出的自适应粒子比优化方法不仅能提高检测跟踪精度,而且能有效提高低信噪比(SNR)条件下的跟踪收敛速度。
Particle filter track-before-detect( PF-TBD) method has been widely used to address the problem of dim weak target detection and tacking for air-to-air missile. Generally,the process of particle ratio selection will affect the algorithm performance of convergence rate and tracking accuracy. Aiming at this problem,an adaptive particle ratio optimization algorithm is proposed. This algorithm makes use of the information supplied by the detection results at the previous moment to get the proper particle ratio.Compared to the conventional method of fixed particle ratio,this method can adjust the particle ratio adaptively according to the history detection results. The simulation results show that this optimization algorithm can not only improve detection tracking accuracy,but also increase the tracking convergence rate,especially in a low SNR environment.
出处
《航空兵器》
2017年第5期25-30,共6页
Aero Weaponry
基金
航空工业创新基金项目(2014C01407R)
关键词
检测前跟踪
粒子滤波
粒子比
粒子数
优化算法
弱小信号检测
空空导弹
track-before-detect
particle filter
particle ratio
particle number
optimization algorithm
dim weak target detection
air-to-air missile
