Box Particle Filter δ-GLMB Algorithm for Multiple Maneuvering Group Targets Tracking

GAN Linhai; WANG Gang; LI Zhihui; SUN Wen; WANG Baotang

doi:10.11999/JEIT251273

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GAN Linhai, WANG Gang, LI Zhihui, SUN Wen, WANG Baotang. Box Particle Filter δ-GLMB Algorithm for Multiple Maneuvering Group Targets Tracking[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251273

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GAN Linhai, WANG Gang, LI Zhihui, SUN Wen, WANG Baotang. Box Particle Filter δ-GLMB Algorithm for Multiple Maneuvering Group Targets Tracking[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251273

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Box Particle Filter δ-GLMB Algorithm for Multiple Maneuvering Group Targets Tracking

doi: 10.11999/JEIT251273 cstr: 32379.14.JEIT251273

1.
Air Defense and Antimissile School, Air Force Engineering University, Xi’an, 710051, China
2.
College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China
3.
Unit 94654 of PLA, Nanjing 210023, China

Received Date: 2025-11-29
Accepted Date: 2026-05-29
Rev Recd Date: 2026-05-29

Available Online: 2026-06-10

Abstract

Abstract

Objective Targets that move in a coordinated manner or show similar motion patterns are commonly referred to as group targets. Dense group targets contain many closely spaced individuals and often suffer from poor measurement resolvability, severe measurement overlap, and frequent target disappearance and reappearance. These factors make it difficult to establish stable tracks for individual targets within the group. Such groups are therefore usually treated as a whole to jointly estimate the kinematic state of the centroid and the extended shape. To improve tracking accuracy and computational efficiency for multiple maneuvering group targets under nonlinear measurements, an Interacting Multiple Model Gamma Box Particle δ-Generalized Labeled Multi-Bernoulli (IMM-GBP-δ-GLMB) algorithm is proposed. Tracking efficiency under nonlinear measurements is improved using the Box Particle Filter (BPF). The likelihood function of the GBP algorithm is improved, and the IMM algorithm is introduced to enhance tracking of the extended shape and centroid kinematic state of group targets. Finally, the method is integrated with the GLMB filter to track an unknown number of multiple maneuvering group targets. Methods Existing algorithms mainly describe the area-based overlap between the predicted extended state of group targets and the measurement distribution, but they do not fully capture shape similarity. To address this limitation, the likelihood function of the BPF is modified. Geometric parameters, including the semi-major axis, semi-minor axis, and inclination angle, are incorporated into the likelihood function. This improves the modeling of similarity between the predicted extended state and the measurement distribution. The modification is particularly useful for maneuvering group targets, because the inclination angle of the extended shape changes frequently during maneuvering. Based on IMM modeling of group motion, a model index is added to the centroid kinematic state of each box particle. The model index and centroid kinematic state are jointly estimated in each iteration, allowing mode transitions of individual box particles to be tracked and further improving tracking accuracy. The improved IMM-GBP filter is then embedded into the labeled random finite set framework, and the IMM-GBP-δ-GLMB algorithm is derived for effective tracking of multiple maneuvering group targets. Results and Discussions Simulation experiments are conducted to compare the proposed IMM-GBP-δ-GLMB algorithm with the IMM Sequential Monte Carlo δ-GLMB (IMM-SMC-δ-GLMB) filter. The proposed algorithm maintains comparable estimation accuracy for the centroid state, extended state, measurement rate, and number of targets, while improving computational efficiency. In the given simulation scenario, the proposed algorithm achieves a 3.8-fold improvement in timeliness, with an approximately 8.5% reduction in tracking accuracy. In scenarios with two and three group targets, the average tracking time growth rate of the proposed algorithm is 96% of that of the IMM-SMC-δ-GLMB filter. This result indicates good temporal robustness as the number of group targets increases. Therefore, the proposed algorithm has strong practical value. Conclusions This paper addresses the tracking of multiple maneuvering group targets under nonlinear measurement conditions by proposing the IMM-GBP-δ-GLMB algorithm. The main contributions are as follows: (1) The likelihood function of the BPF is improved to strengthen the measurement of similarity between the target extended shape and the measurement distribution, improving the tracking accuracy of group target states. (2) A motion model label is assigned to each box particle, and transitions in the target motion state are tracked during filtering. This allows the filter to achieve higher tracking accuracy with fewer box particles and improves computational efficiency. (3) The IMM-GBP method is integrated into the δ-GLMB framework to obtain the final IMM-GBP-δ-GLMB filter, which realizes effective tracking of multiple maneuvering group targets.
- Group target,
- Generalized labeled multi-Bernoulli,
- Box particle filter,
- Interacting multiple model,
- Gamma Gaussian inverse Wishart

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References(19)

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