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)