采用粒子滤波的非相干积累检测方法

Particle Filter based Non-coherent Integration Method for Detection

  • 摘要: 本文提出一种新的基于粒子滤波的检测前跟踪算法,即基于粒子滤波的非相干积累检测方法,以实现对微弱目标的有效检测。该方法通过粒子滤波的重采样过程搜寻可能的目标轨迹,沿可能的目标轨迹累积观测数据的绝对值,当最大的累积绝对值超过给定门限时认为目标存在,同时输出最大累积对应的状态序列作为状态估计结果。该方法具有较好的检测性能,且其检测性能可通过极值统计理论分析。相对于其它基于粒子滤波的检测前跟踪算法,本文提出方法的检测统计量服从广义极值分布,因此可获得检测概率、虚警概率以及门限的解析表达式,可有效减少检测门限估计时的运算量。基于天波超视距雷达目标检测及状态估计的仿真证明了该方法性能和效率。

     

    Abstract: In this paper a novel particle filter based track-before-detect algorithm, namely a particle filter based incoherent integration method for detection, is proposed for detection of weak target. In the proposed method, a target was declared to be present if the maximum accumulation of the absolute measurements along the possible tracks exceeds a given threshold, where the possible tracks were searched according to the resampling procedure in particle filter. The estimated state sequence of target could be output as the target is declared to be present. The detection performance of the proposed method was well enough and could be analyzed via extreme value theory. Thus the explicit expressions such as the probabilities of detection and false alarm and detection threshold can be obtained. An example of sky wave over-the-horizon radar target detection and tracking is present to demonstrate the capability and efficiency of the proposed method.

     

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