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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

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

Uncertain maneuvering target trackingProbability Hypothesis Density(PHD)filterInteractive Multi-Model(IMM)Translation and rotationBearings-Only Tracking(BOT)

薛昱、冯西安

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西北工业大学航海学院 西安 710072

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

2024

电子与信息学报
中国科学院电子学研究所 国家自然科学基金委员会信息科学部

电子与信息学报

CSTPCD北大核心
影响因子:1.302
ISSN:1009-5896
年,卷(期):2024.46(11)