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首页> 外文期刊>Radar, Sonar & Navigation, IET >Joint detection and tracking algorithm for cognitive radar based on parallel structure of EKF and particle filter
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Joint detection and tracking algorithm for cognitive radar based on parallel structure of EKF and particle filter

机译：基于EKF和粒子滤波并行结构的认知雷达联合检测与跟踪算法

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摘要

In order to reduce the uncertainty of radar manoeuvring target tracking (RMTT) in cluttered background, a joint detection and tracking algorithm based on cognitive radar is proposed. First, a prism structure resolution cell of time-delay, Doppler and azimuth is designed. Then, an approximate expression of measurement error covariance including waveform and detection threshold parameters is given. Then, based on the idea of human brain perception-action cycle, a joint waveform and detection threshold adaptive tracking algorithm based on minimum information entropy criterion is proposed. Finally, a cognitive structure adaptive particle filter (CSAPF) algorithm, based on parallel structure of extended Kalman filter (EKF) and particle filter, are used with Probabilistic Data Association (PDA) algorithm for RMTT. During the process, CSAPF-PDA can always obtain the best tracking performance with the minimum number of particle samples, thus effectively taking into account the tracking accuracy and efficiency. The effectiveness of the proposed algorithm is verified by simulation experiments.
机译：为了减少杂波背景下雷达机动目标跟踪的不确定性，提出了一种基于认知雷达的联合检测与跟踪算法。首先，设计了时滞，多普勒和方位角的棱镜结构分辨单元。然后，给出了包括波形和检测阈值参数在内的测量误差协方差的近似表达式。然后，基于人脑感知－动作周期的思想，提出了一种基于最小信息熵准则的联合波形与检测阈值自适应跟踪算法。最后，基于扩展卡尔曼滤波器（EKF）和粒子滤波器的并行结构的认知结构自适应粒子滤波器（CSAPF）算法与概率数据关联（PDA）算法一起用于RMTT。在此过程中，CSAPF-PDA始终能够以最少的颗粒样本数量获得最佳的跟踪性能，从而有效地考虑了跟踪精度和效率。仿真实验验证了该算法的有效性。

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