Multi-target Behavior and Intent Prediction on the Ground Under Incomplete Perception Conditions

ZHU Xinyi; PING Peng; HOU Wanying; SHI Quan; WU Qi

doi:10.11999/JEIT250322

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ZHU Xinyi, PING Peng, HOU Wanying, SHI Quan, WU Qi. Multi-target Behavior and Intent Prediction on the Ground Under Incomplete Perception Conditions[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250322

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ZHU Xinyi, PING Peng, HOU Wanying, SHI Quan, WU Qi. Multi-target Behavior and Intent Prediction on the Ground Under Incomplete Perception Conditions[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250322

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Multi-target Behavior and Intent Prediction on the Ground Under Incomplete Perception Conditions

doi: 10.11999/JEIT250322 cstr: 32379.14.JEIT250322

ZHU Xinyi¹,
PING Peng^{1
,
,},
HOU Wanying²,
SHI Quan¹,
WU Qi³

1.
School of Transportation and Civil Engineering, Nantong University, Nantong, Jiangsu 226001, China
2.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
3.
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Funds: The National Natural Science Foundation of China (52442218, U2433216, 52202496)

Received Date: 2025-04-27
Rev Recd Date: 2025-08-28

Available Online: 2025-09-02

Abstract

Abstract

Objective Modern battlefield environments, characterized by complex and dynamically uncertain target behaviors combined with information asymmetry, present significant challenges for intent prediction. Conventional methods lack robustness in processing incomplete data, rely on oversimplified behavioral models, and fail to capture tactical intent semantics or adapt to rapidly evolving multi-target coordinated scenarios. These limitations restrict their ability to meet the demands of real-time recognition of high-value target intent and comprehensive ground target situational awareness. To address these challenges, this study proposes a Threat Field-integrated Gated Recurrent Unit model (TF-GRU), which improves prediction accuracy and robustness through threat field modeling, dynamic data repair, and multi-target collaboration, thereby providing reliable support for battlefield decision-making. Methods The TF-GRU framework integrates static and dynamic threat field modeling with a hybrid Particle Filtering (PF) and Dynamic Time Warping (DTW) strategy. Static threat fields quantify target-specific threats (e.g., tanks, armored vehicles, artillery) using five factors: enemy–friend distance, range, firepower, defense, and mobility. Gaussian and exponential decay models are employed to describe spatial threat diffusion across different target categories. Dynamic threat fields incorporate real-time kinematic variables (velocity, acceleration, orientation) and temporal decay, allowing adaptive updates of threat intensity. To address incomplete sensor data, a PF–DTW switching mechanism dynamically alternates between short-term PF (N = 1,000 particles) and long-term historical trajectory matching (DTW with β = 50). Collaborative PF introduces neighborhood angular constraints to refine multi-target state estimation. The GRU architecture is further enhanced with Mish activation, adaptive Xavier initialization, and threat-adaptive gating, ensuring effective fusion of trajectory and threat features. Results and Discussions Experiments were conducted on a simulated dataset comprising 150 trajectories and 270,000 timesteps. Under complete data conditions, the TF-GRU model achieved the highest accuracy on both the training and test sets, reaching 94.7% and 92.9%, respectively, indicating strong fitting capability and generalization performance (Fig. 10). After integrating static and dynamic threat fields, model accuracy increased from 72% (trajectory-only input) to 83%, accompanied by substantial improvements in F1 scores and reductions in predictive uncertainty (Fig. 7). In scenarios with missing data, TF-GRU maintained an accuracy of 86.2%, outperforming comparative models and demonstrating superior robustness (Fig. 11). These results confirm that the PF–DTW mechanism effectively reduces the adverse effects of both short-term and long-term data loss, while the collaborative PF strategy strengthens multi-target prediction through neighborhood synergy (η = 0.6). This combination enables robust threat field reconstruction and reliable intent inference (Figs. 8–9). Conclusions The TF-GRU model effectively addresses the challenges of intent prediction in complex battlefield environments with incomplete data through threat field modeling, the PF–DTW dynamic repair mechanism, and multi-target collaboration. It achieves high accuracy and robustness, providing reliable support for situational awareness and command decision-making. Future work will focus on applying the model to real-world datasets and enhancing computational efficiency to facilitate practical deployment.
- Intent prediction,
- Situational awareness,
- Particle Filtering (PF),
- Gated Recurrent Unit (GRU),
- Threat field

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References(33)

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