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针对低信杂比环境下的多机动目标跟踪问题,提出了一种基于极大似然(ML)背景参数估计的中心差分卡尔曼-势概率假设密度滤波(BE-CDKF-CPHD)算法。算法采用ML法实时估计重尾分布模型参数,计算检测概率和虚警概率。运用极大似然-恒虚警(MLCFAR)算法对信号进行处理,提取有效量测值,将幅值似然函数与势概率假设密度滤波器(CPHD)中的目标位置似然函数相结合,通过中心差分法递归更新得到后验均值与协方差,达到对多机动目标进行跟踪的目的。仿真结果表明,在低信杂比环境中,所提算法提高了跟踪精度与目标数目估计准确度。
Aiming at tracking multi-maneuvering targets in low signal-to-noise ratio (SNR) environment, a central difference Kalman-potential probability hypothesis density filter (BE-CDKF-CPHD) algorithm based on maximum likelihood (ML) The ML method is used to estimate the parameters of heavy-tailed distribution model in real time, and the detection probability and false alarm probability are calculated. The signal is processed by MLCFAR algorithm, the effective measurement value is extracted, the amplitude likelihood function is combined with the target position likelihood function in potential probability density filter (CPHD) The central difference method is recursively updated to obtain the posterior mean and covariance to achieve the goal of tracking multiple maneuvering targets. The simulation results show that the proposed algorithm improves the tracking accuracy and the accuracy of the target number estimation in low SNR environment.