A 3D Underwater Target Tracking Algorithm with Integrated Grubbs-Information Entropy and Improved Particle Filter

CAI Fanglin; WANG Ji; QIU Haowei

doi:10.11999/JEIT250249

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CAI Fanglin, WANG Ji, QIU Haowei. A 3D Underwater Target Tracking Algorithm with Integrated Grubbs-Information Entropy and Improved Particle Filter[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250249

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CAI Fanglin, WANG Ji, QIU Haowei. A 3D Underwater Target Tracking Algorithm with Integrated Grubbs-Information Entropy and Improved Particle Filter[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250249

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A 3D Underwater Target Tracking Algorithm with Integrated Grubbs-Information Entropy and Improved Particle Filter

doi: 10.11999/JEIT250249 cstr: 32379.14.JEIT250249

1.
School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
2.
Research Center of Guangdong Smart Oceans Sensor Networks and Equipment Engineering, Zhanjiang 524088, China

Funds: Key Areas Special Project of New Generation Information Technology for Higher Education Institutions in Guangdong Province (2020ZDZX3008)

Received Date: 2025-04-09
Rev Recd Date: 2025-10-22

Available Online: 2025-10-27

Abstract

Abstract

Objective To address the limited target tracking accuracy of traditional Particle Filter (PF) algorithms in three-dimensional Underwater Wireless Sensor Networks (UWSNs) under abnormal conditions, this study proposes a three-dimensional underwater target tracking algorithm (OGIE-IPF). The algorithm integrates an optimized Grubbs criterion–based information entropy–weighted data fusion with an Improved Particle Filter (IPF). Conventional PF algorithms often suffer from particle degeneracy and impoverishment, which restrict estimation accuracy. Although weight optimization strategies introduced during resampling can enhance particle diversity, existing approaches mainly rely on fixed weighting factors that cannot dynamically adapt to real-time operating conditions. Moreover, current anomaly detection methods for multi-source data fusion fail to effectively address data coupling and heteroscedasticity across dimensions. To overcome these challenges, a dynamic adaptive hierarchical weight optimization strategy is designed for the resampling phase, enabling adaptive particle weighting across hierarchy levels. Additionally, a Mahalanobis distance discrimination mechanism is incorporated into the Grubbs criterion–based anomaly detection method, achieving effective multi-dimensional anomaly detection through covariance-sensitive analysis. Methods The proposed OGIE-IPF algorithm enhances target tracking accuracy under underwater abnormal conditions through a distributed data processing framework that integrates multi-source data fusion and adaptive filtering. First, the Unscented Kalman Filter (UKF) is incorporated into the particle filtering framework to construct the importance density function, thereby alleviating particle degeneracy. Simultaneously, a dynamic adaptive hierarchical weight optimization mechanism is proposed during the resampling phase to improve particle diversity. Second, the Mahalanobis distance replaces the conventional standardized residual method in the standard Grubbs criterion for anomaly statistic construction. By incorporating the covariance matrix of multidimensional variables, the method achieves effective anomaly detection for multi-dimensional data. Finally, local target tracking is performed using the IPF combined with the optimized Grubbs criterion for anomaly detection and sensor credibility evaluation, whereas global state estimation is realized through an information entropy–weighted multi-source fusion algorithm. Results and Discussions The IPF developed in this study is designed to enhance particle set diversity through optimization of the importance density function and refinement of the resampling strategy. To evaluate algorithm performance, a comparative experimental group with a particle population of 100 is established. Simulation results indicate that the weight distribution variances of the IPF at specific time points and over the entire tracking period are reduced by approximately 98.27% and 97.26%, respectively, compared with the traditional PF (Figs. 3 and 4). These findings suggest that the improved strategy effectively regulates particles with varying weights, resulting in a balanced distribution across hierarchical weight levels. Sensor anomalies are simulated by introducing substantial perturbations in observation noise. The experimental data show that the OGIE-IPF algorithm maintains optimal error metrics throughout the operational period (Figs. 5 and 6), demonstrating superior capability in suppressing abnormal noise interference. To further assess algorithm robustness, two representative scenarios under low-noise and high-noise conditions are constructed for multi-algorithm comparison. The results indicate that OGIE-IPF achieves Root Mean Square Error (RMSE) reductions of 79.78%, 66.78%, and 56.41% compared with the PF, Extended Particle Filter (EPF), and Unscented Particle Filter (UPF) under low-noise conditions, and reductions of 83.41%, 70.38%, and 21.68% under high-noise conditions (Figs. 9 and 12). Conclusions The OGIE-IPF algorithm proposed in this study enhances target tracking accuracy in three-dimensional underwater environments through two synergistic mechanisms. First, tracking precision is improved by refining the PF framework to optimize the intrinsic accuracy of the filtering process. Second, data fusion reliability is strengthened via an anomaly detection framework that mitigates interference from erroneous sensor measurements. Simulation results confirm that the OGIE-IPF algorithm produces state estimations more consistent with ground truth trajectories than conventional PF, EPF, and UPF algorithms, achieving lower RMSE and maintaining stable tracking performance under limited particle populations and abnormal noise conditions. Future work will extend the model to incorporate dynamic marine environmental factors and address the effects of malicious node interference within underwater network security systems.
- Underwater Wireless Sensor Network (UWSN),
- Target tracking,
- Particle Filter (PF),
- Grubbs criterion,
- Information entropy

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References(24)

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