Multiple model particle filter track-before-detect for range ambiguous radar
Multiple model particle filter track-before-detect for range ambiguous radar
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DOI：
10.1016/j.cja.2013.07.033
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发表时间：
2013-12
影响因子：
5.7
通讯作者：
Guo-Hong Wang;Shuncheng Tan;Chengbin Guan;Na Wang;Zhao-hui Liu
中科院分区：
文献类型：
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作者：
Guo-Hong Wang;Shuncheng Tan;Chengbin Guan;Na Wang;Zhao-hui Liu
机载脉冲多普勒(PD)雷达系统普遍采用中、高脉冲重复频率(MPRF)和高脉冲重复频率(HPRF)模式，造成目标距离测量模糊的问题。现有的基于数据处理的距离模糊解算方法在信噪比足够高的情况下工作良好。针对低信噪比环境下距离模糊雷达的目标检测与跟踪问题，提出了一种基于多模型粒子滤波(MMPF)的检测前跟踪方法。通过引入表示目标是否存在的离散变量和离散脉冲间隔数(PIN)作为目标状态向量的分量，并将PIN的增量变量建模为三状态马尔可夫链，该算法将距离模糊的求解问题转化为混合状态滤波问题。最后，在贝叶斯框架下，采用基于MMPF的TBD方法实现了混合滤波问题。仿真结果表明，所提出的贝叶斯方法能够同时估计目标状态和PIN，成功地对距离模糊雷达中的弱目标进行了检测和跟踪。仿真结果还表明，该方法在低信噪比环境下的性能优于多假设(MH)方法。
The middle pulse repetition frequency (MPRF) and high pulse repetition frequency (HPRF) modes are widely adopted in airborne pulse Doppler (PD) radar systems, which results in the problem that the range measurement of targets is ambiguous. The existing data processing based range ambiguity resolving methods work well on the condition that the signal-to-noise ratio (SNR) is high enough. In this paper, a multiple model particle filter (MMPF) based track-before-detect (TBD) method is proposed to address the problem of target detection and tracking with range ambiguous radar in low-SNR environment. By introducing a discrete variable that denotes whether a target is present or not and the discrete pulse interval number (PIN) as components of the target state vector, and modeling the incremental variable of the PIN as a three-state Markov chain, the proposed algorithm converts the problem of range ambiguity resolving into a hybrid state filtering problem. At last, the hybrid filtering problem is implemented by a MMPF-based TBD method in the Bayesian framework. Simulation results demonstrate that the proposed Bayesian approach can estimate target state as well as the PIN simultaneously, and succeeds in detecting and tracking weak targets with the range ambiguous radar. Simulation results also show that the performance of the proposed method is superior to that of the multiple hypothesis (MH) method in low-SNR environment.