Cooperative Search and Tracking of Moving Ships Using Constellation Multifunctional Payloads Based on Dynamic Information Gain

ZHANG Yumo; ZHAO Fuhai; LI Xiaobin; FAN Shenghua; QU Tao

doi:10.11999/JEIT260500

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ZHANG Yumo, ZHAO Fuhai, LI Xiaobin, FAN Shenghua, QU Tao. Cooperative Search and Tracking of Moving Ships Using Constellation Multifunctional Payloads Based on Dynamic Information Gain[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260500

Citation:

ZHANG Yumo, ZHAO Fuhai, LI Xiaobin, FAN Shenghua, QU Tao. Cooperative Search and Tracking of Moving Ships Using Constellation Multifunctional Payloads Based on Dynamic Information Gain[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260500

Citation:

ZHANG Yumo, ZHAO Fuhai, LI Xiaobin, FAN Shenghua, QU Tao. Cooperative Search and Tracking of Moving Ships Using Constellation Multifunctional Payloads Based on Dynamic Information Gain[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260500

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Cooperative Search and Tracking of Moving Ships Using Constellation Multifunctional Payloads Based on Dynamic Information Gain

doi: 10.11999/JEIT260500 cstr: 32379.14.JEIT260500

1.
School of Computer Science, Wuhan University, Wuhan 430072, China
2.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
3.
Satellite Information Intelligent Processing and Application Research Laboratory, Beijing 100085, China

Funds: The National Natural Science Foundation of China (82571371)

Received Date: 2026-04-23
Accepted Date: 2026-08-24
Rev Recd Date: 2026-08-21

Available Online: 2026-08-29

Abstract

Abstract

Objective Wide-area maritime surveillance requires satellite constellations to search for and revisit noncooperative maneuvering ships whose positional uncertainty increases during periods without successful observation. Meanwhile, multifunctional payloads are subject to strongly coupled constraints on observation timing, attitude maneuvering, payload mode, and energy consumption. To address dynamic target uncertainty while ensuring executable constellation scheduling, a cooperative search-and-tracking method based on dynamic information gain is proposed. Methods A closed-loop rolling-horizon framework inspired by model predictive control is constructed for prediction, optimization, execution, and feedback. At each rolling decision epoch, candidate atomic observation tasks are generated within the planning horizon, whereas only tasks within the current execution window are committed. Each task specifies the executing satellite, target, candidate pointing grid, start and end times, and payload mode. Target uncertainty is represented by a parameterized probability grid generated from the last confirmed state, speed and heading perturbations, and elapsed time since the last successful observation. Hit/Miss feedback updates the uncertainty baseline for the next rolling decision epoch, at which the probability grid, candidate tasks, and observation plan are regenerated. A state-driven dual-mode benefit model is established based on target information entropy and the number of consecutive Hits. In the robust tracking state, tasks in the narrow-field-of-view mode are evaluated using the prior capture probability, defined as the probability mass covered by the task footprint. In the lost-search state, tasks in the wide-field-of-view mode are evaluated using binary Hit/Miss event entropy as an approximation of the search information value. This approximation is based on the information-theoretic definition of Kullback-Leibler divergence and avoids explicit posterior reconstruction for each candidate task. A dynamic priority coefficient is introduced to increase the scheduling urgency of targets that have remained unobserved for longer periods and to moderately reduce the priority of repeatedly confirmed targets. The resulting multi-objective model maximizes weighted task benefit and information gain while minimizing energy consumption. Hard constraints are imposed on single-satellite temporal exclusivity, attitude-switching stabilization time, and energy budget. Based on Non-dominated Sorting Genetic Algorithm II (NSGA-II), the Cooperative Evolutionary Planning-Multi-Objective (CEP-MO) algorithm employs global integer-index encoding, constraint-aware Top-K heuristic initialization, satellite-group crossover, and an adaptive repair operator to improve feasible-solution generation and cooperative planning efficiency. The objective values of feasible Pareto solutions are normalized, and the solution closest to the ideal point (1, 1, 0) is selected for execution. Results and Discussions Simulations using a 48-satellite Walker Delta constellation demonstrate the effectiveness of the proposed method. In the 200-target scenario, Standard NSGA-II yields an average revisit interval of 53.5 min and a weighted coverage rate of 13.9%, whereas CEP-MO achieves 19.9 min and 41.7%, respectively. The average revisit interval is therefore reduced by 62.8% (Fig. 7). Removing the binary Hit/Miss event entropy benefit increases the system average uncertainty and average revisit interval. Replacing constraint-aware initialization with random initialization also degrades early-stage planning performance and the weighted coverage rate. As the number of targets increases from 100 to 200, CEP-MO maintains acceptable scalability (Fig. 8). At 200 targets, its weighted coverage rate is 16.6 percentage points higher than that of CEP-MO w/o Entropy. The computation time per rolling decision is approximately 22 s, which is shorter than the 10 min rolling period. In addition, the Gini coefficient of remaining satellite energy remains below 0.3, indicating that energy consumption is not excessively concentrated on a small subset of satellites. Conclusions The proposed framework integrates parameterized probability-grid uncertainty representation, state-driven dual-mode benefit evaluation for search and tracking, closed-loop rolling-horizon feedback, and constraint-aware multi-objective evolutionary planning. CEP-MO reduces the average revisit interval and improves the weighted coverage rate while satisfying single-satellite temporal exclusivity, attitude-switching stabilization, and energy-budget constraints. The method provides an effective approach for large-scale resource-constrained maritime surveillance and establishes a basis for future extensions that consider identification errors, communication delays, and constrained inter-satellite links.

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References(34)

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