基于QIPF的弱目标检测前跟踪算法

针对低信噪比条件下弱目标检测跟踪问题,提出一种拟蒙特卡罗智能粒子滤波检测前跟踪算法(Quasi-Monte Carlo Intelligent Particle Filter Track Before Detect,QIPF-TBD)。首先,该算法采用拟蒙特卡罗技术改善探测空间中粒子分布的均匀性;其次,通过对更新阶段的粒子进行交叉变异等操作,提高粒子重采样之后的多样性。与同类算法的仿真分析表明,所提方法能有效改善低信噪比目标的检测概率和跟踪精度。

Track Before Detect Algorithm Based on QIPF

For the problem of detecting and tracking low signal-to-noise-ratio target,a quasi Monte Carlo intelligent particle filter track before detect(QIPF-TBD)algorithm is proposed. First,the quasi Monte Carlo technique is used to improve the distribution of particles in the surveillance region. Second,at the update stage of the particle filter,the crossover and mutation operations are invoked to improve the particle diversity after re-sampling. Compared with other PF-TBD methods in simulations, the proposed method can improve the detection and tracking performance effectively.