针对现有的多目标跟踪（MTT）算法在强杂波环境下会出现严重的性能衰退，利用目标的幅度及多普勒特征，基于随机有限集（RFS）理论，提出了一种幅度及多普勒信息辅助的δ广义标签多伯努利（ADI-δ-GLMB）滤波器。首先，利用幅度及多普勒信息对目标状态进行扩展，在此基础上建立了新的量测似然函数。然后，基于δ-GLMB滤波器框架推导了新的更新方程。最后，进行仿真验证，结果表明：强杂波下，ADI-δ-GLMB滤波器跟踪性能明显优于δ-GLMB滤波器，估计精度及稳定性更高，且计算量更低；同信息辅助的概率假设密度（PHD）、多目标多伯努利（MeMBer）滤波器相比，ADI-δ-GLMB滤波器状态估计精度更高。
To address the severe performance degradation of the existing Multi-Target Tracking (MTT) in high clutter environment, a novel Amplitude and Doppler Information assisted δ-Generalized Labeled Multi-Bernoulli (ADI-δ-GLMB) filter is proposed based on the theory of Random Finite Set (RFS). Firstly, the amplitude and Doppler information isintroduced to expand the target state, and a new function of measurement likelihood is established on this basis. Then, a new update equation is deduced based on the framework of the δ-GLMB filter. Finally, the simulation results show that the tracking performance of the proposed ADI-δ-GLMB filter is significantly better than that of the standard δ-GLMB filter, displaying higher estimation accuracy and stability, and a lower computational cost in dense clutter environment. In addition, the estimation accuracy of the proposed ADI-δ-GLMB filter is higher than that of the information assisted Probability Hypothesis Density (PHD) and Multi-target Multi-Bernoulli (MeMBer) filter.
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