Cooperative Search and Tracking of Moving Ships Using Constellation Multi-Functional 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 Multi-Functional 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 Multi-Functional 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 Multi-Functional 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 Multi-Functional 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.
National Key Laboratory of Satellite Information Intelligent Processing and Application Technology, Beijing 100084, China

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

Accepted Date: 2026-08-24
Rev Recd Date: 2026-08-24

Available Online: 2026-08-29

Abstract

Abstract

Objective Wide-area maritime surveillance requires satellite constellations to search for and revisit non-cooperative maneuvering ships whose positions become uncertain after missed observations. Meanwhile, multi-functional payloads are subject to coupled constraints on observation timing, attitude maneuvering, payload mode, and energy consumption. To address dynamic target uncertainty and 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 to perform prediction, optimization, execution, and feedback. At each decision epoch, candidate atomic tasks are generated over a planning horizon, while only tasks within the current execution window are committed. Each task specifies the executing satellite, target, candidate pointing grid, start/end times, and payload mode. Target uncertainty is represented by a parameterized probabilistic grid derived from the latest 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 step, where the probability grid, candidate tasks, and observation plan are regenerated. A state-driven dual-mode benefit model is established according to target information entropy and consecutive successful observations. In the robust tracking state, narrow-field tasks are evaluated by the prior capture probability, namely the probability mass covered by the task footprint. In the lost-search state, wide-field tasks are evaluated by the binary entropy of Hit/Miss events as an approximation of search information value. This approximation is motivated by Kullback-Leibler divergence and avoids explicit posterior reconstruction for every candidate task. A dynamic priority coefficient increases scheduling urgency for long-unobserved targets and moderately down-weights repeatedly confirmed targets. The resulting multi-objective model maximizes weighted task benefit and information gain while minimizing energy consumption, subject to hard constraints on single-satellite temporal exclusivity, attitude-transition stabilization time, and available energy. Based on 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 adaptive repair to improve feasible-solution generation. Feasible Pareto solutions are normalized, and the solution closest to the ideal point (1,1,0) is selected for execution. Results and Discussions Simulations with a 48-satellite Walker constellation demonstrate the effectiveness of the proposed method. In the 200-target scenario, Standard NSGA-II obtains an average revisit interval of 53.5 min and a weighted coverage of 13.9%, whereas CEP-MO achieves 19.9 min and 41.7%, respectively, reducing the average revisit interval by 62.8% (Fig. 7). Removing the binary-event-entropy benefit increases system-average uncertainty and revisit interval, while replacing constraint-aware initialization with random initialization degrades early convergence and weighted coverage. As the target number increases from 100 to 200, CEP-MO maintains acceptable scalability (Fig. 8). At 200 targets, its weighted coverage is 16.6 percentage points higher than that of CEP-MO w/o Entropy, the computation time per rolling decision is approximately 22 s, and the Gini coefficient of remaining satellite energy stays below 0.3, indicating that energy consumption is not excessively concentrated on a small subset of satellites. Conclusions The proposed framework integrates probabilistic-grid uncertainty representation, state-driven search/tracking benefit evaluation, rolling feedback, and constraint-aware multi-objective evolutionary planning. CEP-MO improves revisit and weighted-coverage performance while maintaining temporal, attitude, and energy feasibility. The method provides an effective approach for large-scale resource-constrained maritime surveillance and a basis for future extensions involving identification errors, communication delays, and constrained inter-satellite links.

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References(34)

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