中文摘要：

在许多多目标跟踪场景中,目标返回的幅度通常强于虚警杂波返回的幅度。通过建立更加准确的包含幅度信息的目标和虚警杂波似然函数,可提高多目标估计精度。该文提出一种基于随机有限集的幅度信息辅助多伯努利滤波（Amplitude Information Assistant Multi-Bernoulli Filter,AIA-MBer F）算法。该算法通过建立幅度似然函数将幅度信息引入到多伯努利滤波的更新过程中,并给出针对线性和非线性模型的高斯混合（Gaussian Mixture,GM）和序贯蒙特卡洛（Sequential Monte Carlo,SMC）实现方法。仿真结果表明,该滤波算法相比于传统多伯努利滤波（Multi-Bernoulli Filter,MBer F）无论GM还是SMC实现都可获得更加准确稳定的目标数和对应的目标状态估计。

英文摘要：

In many multi-target tracking scenarios, the amplitude of target returns are stronger than those coming from false alarms. This amplitude information can be used to improve the multi-target state estimation by obtaining more accurate target and false-alarm likelihoods. In this paper, a novel multi-Bernoulli filtering algorithm is proposed, which is based on the random finite set and incorporate the amplitude information. The amplitude likelihood functions are derived to incorporate the amplitude information into the multi-Bernoulli filter in the update step. In addition, a Gaussian Mixture（GM） implementation for the linear model and a Sequential Monte Carlo（SMC） implementation for the non-linear model are proposed. Simulation results for Gaussian Mixture and Sequential Monte Carlo implementations show that the proposed filter demonstrates a significant improvement than conventional multi-Bernoulli filter in the estimation accuracy of both the number of targets and their states.