Topology Optimization Method for UAV Swarm SAR Using Illuminators of Opportunity from LEO Communication Satellites

ZHOU Song; DU Xiankang; WANG Yuhao; WEN Pin; YANG Lei; XING Mengdao

doi:10.11999/JEIT260676

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ZHOU Song, DU Xiankang, WANG Yuhao, WEN Pin, YANG Lei, XING Mengdao. Topology Optimization Method for UAV Swarm SAR Using Illuminators of Opportunity from LEO Communication Satellites[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260676

Citation:

ZHOU Song, DU Xiankang, WANG Yuhao, WEN Pin, YANG Lei, XING Mengdao. Topology Optimization Method for UAV Swarm SAR Using Illuminators of Opportunity from LEO Communication Satellites[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260676

Citation:

ZHOU Song, DU Xiankang, WANG Yuhao, WEN Pin, YANG Lei, XING Mengdao. Topology Optimization Method for UAV Swarm SAR Using Illuminators of Opportunity from LEO Communication Satellites[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260676

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Topology Optimization Method for UAV Swarm SAR Using Illuminators of Opportunity from LEO Communication Satellites

doi: 10.11999/JEIT260676 cstr: 32379.14.JEIT260676

1.
College of Information Engineering, Nanchang University, Nanchang 330031, China
2.
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
3.
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (62561040, 62271487, 62561038, 62573220), Jiangxi Provincial Key Research and Development Program Project (20243BBG71030, 20244BBG73002, 20252BCE310047)

Accepted Date: 2026-09-15
Rev Recd Date: 2026-09-15

Available Online: 2026-09-18

Abstract

Abstract

Objective Low Earth Orbit Communication Satellite Constellations (LEO-COS) provide globally distributed, continuously available communication signals, which offer a promising opportunity for passive Synthetic Aperture Radar (SAR) imaging. However, when LEO-COS signals are used as opportunistic illuminators, the limited effective bandwidth and relatively low Signal-to-Noise Ratio (SNR) restrict the imaging capability of conventional single-receiver SAR systems. To overcome these limitations, this paper proposes a novel “constellation–swarm” cooperative SAR imaging mode that integrates LEO-COS illuminators with an Unmanned Aerial Vehicle (UAV) swarm. In this mode, multiple UAV receivers cooperatively collect echo signals, enabling spectrum extension and coherent energy accumulation. Since the spatial configuration of the UAV swarm directly determines the wavenumber spectrum distribution and ultimately affects SAR imaging quality, it is necessary to optimize the UAV swarm configuration under dynamic and heterogeneous bistatic observation geometries. This study aims to establish a configuration optimization method for UAV swarm SAR using LEO-COS opportunistic illumination, thereby improving spectrum utilization, imaging resolution, and robustness in complex observation scenarios. Methods The relationship between UAV swarm spatial configuration and SAR imaging performance is first analyzed from the perspective of wavenumber spectrum geometry. Based on the echo model of the “constellation–swarm” SAR system, the influence of UAV positions and motion parameters on spectrum distribution is derived. The configuration design problem is then formulated as a multi-objective optimization problem by considering spectrum orthogonality, spectrum parallelism, spectrum misalignment suppression, and minimum imaging SNR. To solve this high-dimensional and strongly coupled optimization problem, an Associated-Structure-Encoding Nondominated Sorting Genetic Algorithm II (AS-NSGA-II) is proposed. In the proposed algorithm, the associated structure encoding mechanism is used to preserve the geometric coupling relationship among configuration variables during crossover and mutation. Layered Cubic chaotic mapping is introduced to initialize the population, which improves the uniformity and diversity of candidate solutions in the search space. In addition, an adaptive mutation strategy is designed to dynamically adjust the mutation probability during evolution, so that the algorithm can balance global exploration and local exploitation while maintaining configuration feasibility. Results and Discussions Simulation results show that the optimized UAV swarm configurations can effectively rearrange the wavenumber spectrum support areas of different receiving nodes, reduce spectrum overlap and misalignment, and improve spectrum stitching quality(Fig. 5). In comparison with conventional Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods, the proposed AS-NSGA-II algorithm shows faster and more stable convergence. The convergence curves of the four objective functions demonstrate that AS-NSGA-II reaches a stable optimization state after approximately 60 generations, while GA and PSO exhibit slower convergence and larger fluctuations(Fig. 6). Area target imaging results further show that the proposed method suppresses false grating lobes more effectively and preserves the target edge and structural features more clearly(Fig. 7). Under low-SNR conditions, the optimized configuration maintains reliable imaging quality through multi-aperture coherent integration, demonstrating better robustness than the comparison methods. Conclusions This paper investigates the UAV swarm configuration optimization problem for “constellation–swarm” cooperative SAR imaging using LEO-COS signals as opportunistic illuminators. By analyzing the coupling relationship between spatial configuration and wavenumber spectrum distribution, a multi-objective optimization model is established to jointly consider geometric spectrum constraints and imaging SNR. The proposed AS-NSGA-II algorithm improves the optimization process by incorporating associated structure encoding, Layered Cubic chaotic initialization, and adaptive mutation. Simulation results demonstrate that the proposed method outperforms conventional GA and PSO methods in convergence speed, optimization stability, spectrum stitching performance, and imaging quality. The optimized UAV swarm configuration can effectively compensate for the bandwidth and SNR limitations of LEO-COS opportunistic illumination and enhance the resolution and robustness of cooperative SAR imaging. This work provides a feasible technical approach for future wide-area, high-resolution, and robust SAR imaging based on integrated satellite–UAV swarm systems.
- Synthetic Aperture Radar (SAR),
- Low Earth Orbit communication satellites (LEO-COS),
- UAV swarm,
- Multi-objective optimization,
- Genetic algorithm

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References(20)

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