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

XUE Yu; FENG Xi’an

doi:10.11999/JEIT240201

Volume 46 Issue 11

Nov. 2024

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XUE Yu, FENG Xi’an. Joint Multi-Gaussian Mixture Probability Hypothesis Density Filter for Bearings-only Multi-target Tracking[J]. Journal of Electronics & Information Technology, 2024, 46(11): 4295-4304. doi: 10.11999/JEIT240201

Citation:

XUE Yu, FENG Xi’an. Joint Multi-Gaussian Mixture Probability Hypothesis Density Filter for Bearings-only Multi-target Tracking[J]. Journal of Electronics & Information Technology, 2024, 46(11): 4295-4304. doi: 10.11999/JEIT240201

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

doi: 10.11999/JEIT240201

XUE Yu,
FENG Xi’an^,

School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China

Funds: The National Natural Science Foundation of China (62071386)

Received Date: 2024-03-25
Rev Recd Date: 2024-09-29

Available Online: 2024-10-12

Publish Date: 2024-11-10

Abstract

Abstract

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.

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References(27)

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