一种纯方位多目标跟踪的联合多高斯混合概率假设密度滤波器  

Joint Multi-Gaussian Mixture Probability Hypothesis Density Filter for Bearings-only Multi-target Tracking

作　　者：薛昱 冯西安[1] XUE Yu;FENG Xi’an(School of Marine Science and Technology,Northwestern Polytechnical University,Xi’an 710072,China)

机构地区：[1]西北工业大学航海学院,西安710072

出　　处：《电子与信息学报》2024年第11期4295-4304,共10页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(62071386)。

摘　　要：现有的多模型-高斯混合-概率假设密度(MM-GM-PHD)滤波器被广泛用于不确定机动目标跟踪,但它不能在不同模型下保持并行的估计,导致各模型的似然值滞后于目标机动。为此,该文提出一种联合多高斯混合概率假设密度(JMGM-PHD)滤波器,并将其用于纯方位多目标跟踪。首先,推导了JMGM模型,其中每个单目标状态估计由一组并行的、带模型概率的高斯函数描述,该状态估计的概率由一个非负的权重来表征。一组权值、模型概率、均值和协方差被统称为JMGM分量。根据贝叶斯规则,推导了JMGM分量的更新方法。然后,利用JMGM模型近似多目标PHD。根据交互式多模型(IMM)规则,推导出JMGM分量的交互、预测和估计方法。将所提JMGM-PHD滤波器应用于纯方位跟踪(BOT)时,针对同时执行平移和旋转的观测站,基于复合函数求导规则推导出一种计算线性化观测矩阵的方法。所提JMGM-PHD滤波器保持了单模型PHD滤波器的形式,但能够自适应地跟踪不确定机动目标。仿真结果表明,JMGM-PHD滤波器克服了似然值滞后于目标机动的问题,在跟踪精度和计算成本方面均优于MM-GM-PHD滤波器。The Multi-Model Gaussian Mixture-Probability Hypothesis Density(MM-GM-PHD)filter is widely used in uncertain maneuvering target tracking,but it does not maintain parallel estimates under different models,leading to the model-related likelihood lagging behind unknown target maneuvers.To solve this issue,a Joint Multi-Gaussian Mixture PHD(JMGM-PHD)filter is proposed and applied to bearings-only multi-target tracking in this paper.Firstly,a JMGM model is derived,where each single-target state estimate is described by a set of parallel Gaussian functions with model probabilities,and the probability of this state estimate is characterized by a nonegative weight.The weights,model-related probabilities,means and covariances are collectively called JMGM components.According to the Bayesian rule,the updating method of the JMGM components is derived.Then,the multi-target PHD is approximated using the JMGM model.According to the Interactive Multi-Model(IMM)rule,the interacting,prediction and estimation methods of the JMGM components are derived.When addressing Bearings-Only Tracking(BOT),a method based on the derivative rule for composite functions is derived to compute the linearized observation matrix of observers that simultaneously performs translations and rotations.The proposed JMGM-PHD filter preserves the form of regular single-model PHD filter but can adaptively track uncertain maneuvering targets.Simulations show that our algorithm overcomes the likelihood lag issue and outperforms the MM-GM-PHD filter in terms of tracking accuracy and computation cost.

关 键 词：不确定机动目标跟踪 概率假设密度滤波器 交互多模型 平移和旋转 纯方位跟踪 

分 类 号：TN911.7[电子电信—通信与信息系统] TP391[电子电信—信息与通信工程]