Time-Series Information-Driven Parallel Interactive Multiple Model Algorithm for Underwater Target Tracking

LAN Chaofeng; ZHANG Tongji; CHEN Huan

doi:10.11999/JEIT250044

Volume 47 Issue 8

Aug. 2025

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LAN Chaofeng, ZHANG Tongji, CHEN Huan. Time-Series Information-Driven Parallel Interactive Multiple Model Algorithm for Underwater Target Tracking[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2685-2693. doi: 10.11999/JEIT250044

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LAN Chaofeng, ZHANG Tongji, CHEN Huan. Time-Series Information-Driven Parallel Interactive Multiple Model Algorithm for Underwater Target Tracking[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2685-2693. doi: 10.11999/JEIT250044

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Time-Series Information-Driven Parallel Interactive Multiple Model Algorithm for Underwater Target Tracking

doi: 10.11999/JEIT250044 cstr: 32379.14.JEIT250044

1.
School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China
2.
Heilongjiang Province Key Laboratory of Pattern Recognition and Information Perception, Harbin University of Science and Technology, Harbin 150080, China
3.
Hanjiang National Laboratory, Wuhan 430000, China

Funds: The National Natural Science Foundation of China (11804068), Heilongjiang Provincial Outstanding Young Teachers Basic Research Support Programme (YQJH2024064)

Received Date: 2025-01-20
Rev Recd Date: 2025-06-30

Available Online: 2025-07-04

Publish Date: 2025-08-27

Abstract

Abstract

Objective Accurate underwater target tracking is critical in marine surveillance, military reconnaissance, and resource management. The nonlinear, stochastic, and uncertain motion of underwater targets, exacerbated by complex environmental dynamics, limits the effectiveness of traditional tracking methods. The Interacting Multiple Model (IMM) algorithm addresses this challenge by integrating several motion models and adaptively switching between them based on the target’s dynamic state. Such adaptability enables improved tracking under abrupt motion transitions, such as submarine maneuvers or the irregular paths of unmanned underwater vehicles. However, the classical IMM algorithm relies on a fixed Transition Probability Matrix (TPM), which can delay model switching and reduce tracking accuracy in highly dynamic settings. To overcome these limitations, this paper proposes an adaptive IMM algorithm that incorporates timing information, parallel processing, and information entropy. These enhancements improve model-switching speed and accuracy, increase adaptability to environmental changes, and boost overall tracking performance and stability. Methods This study proposes a Temporal Information Parallel Interacting Multiple Model (TIP-IMM) target tracking algorithm that integrates temporal information, information entropy evaluation, and parallel processing to adaptively correct the TPM. At each time step, the algorithm identifies the model with the highest probability and assesses whether this model remains dominant across consecutive time steps. If consistent, it is designated the main model. The algorithm then evaluates changes in the main model’s probability relative to other models and updates the TPM based on defined correction rules. A parallel structure is introduced: Module A performs TPM correction, while Module B executes a standard IMM algorithm. Both modules operate concurrently. Information entropy is employed to quantify the uncertainty of the model probability distribution. When entropy is low in Module A, its corrected results are prioritized; when entropy is high, the system places greater reliance on Module B to ensure stable and robust performance under varying conditions. Results and Discussions The proposed TIP-IMM algorithm is evaluated through simulation experiments, demonstrating improved tracking accuracy and stability. True motion and observation trajectories are generated using predefined initial parameters and a motion model. Filtering results from TIP-IMM are compared with those of three benchmark algorithms. The estimated trajectories from TIP-IMM align more closely with the true trajectories, as confirmed by the enlarged views in panels A and B (Fig. 2). Analysis of model probability evolution during filtering indicates that TIP-IMM exhibits smaller fluctuations during model transitions and identifies the dominant model more rapidly than the comparison methods (Fig. 3). To quantify tracking performance, Root Mean Square Error (RMSE) serves as the evaluation metric. TIP-IMM consistently yields lower and smoother RMSE curves across the full trajectory and in both X and Y directions (Figs. 4 and 5). Furthermore, average RMSE (ARMSE) serves as a comprehensive indicator. TIP-IMM achieves lower errors in both position and velocity estimates (Table 2), consistent with the trend observed in RMSE analysis. Although the algorithm incurs a slightly higher runtime than the reference methods, its execution time remains within the millisecond range, meeting the real-time requirements of practical applications (Table 3). Conclusions This study proposes a TIP-IMM algorithm to address limitations of the classical IMM algorithm, particularly model-switching delays and over-smoothing during abrupt target motion changes. By incorporating temporal correlation of model probabilities, parallel processing, and information entropy, TIP-IMM improves responsiveness and transition smoothness in dynamic environments. Simulation experiments confirm that TIP-IMM achieves faster and more accurate model switching than existing methods. Compared with the traditional IMM and benchmark algorithms, TIP-IMM improves overall tracking accuracy by 3.52% to 7.87% across multiple scenarios. It also reduces estimation error recovery time while maintaining high accuracy during sudden motion transitions. These results demonstrate the algorithm’s enhanced adaptability, robustness, and stability, making it well suited for underwater target tracking applications.
- Underwater target tracking,
- Interacting Multiple Model (IMM),
- Transition Probability Matrix (TPM),
- Posterior information,
- Adaptive

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References(26)

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