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SAR multi-satellite collaborative complex area observation planning based on improved genetic algorithm

  • role： First author第一作者
  • Affiliation：

    National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China

  • Email：shixinxd@163.com
  • Introduction：石鑫， 研究方向为SAR多星协同任务规划及SAR图像智能解译。E-mail： shixinxd@163.com
SHI Xin1,  
  • role： Corresponding author通信作者
  • Affiliation：

    National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China

    Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an 710071, China

  • Email：xmd@xidian.edu.cn
  • Introduction：邢孟道，研究方向为雷达成像理论与方法、空时频多维稀疏信号处理、合成孔径激光雷达成像、微波光子合成孔径雷达成像、电磁散射与图像智能处理及智能任务规划等。E-mail： xmd@xidian.edu.cn
XING Mengdao12*,  
  • Affiliation：

    Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an 710071, China

ZHANG Jinsong2,  
  • Affiliation：

    Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an 710071, China

LIU Huitao2,  
  • Affiliation：

    National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China

WANG Hongxian1

resumen

Large-scale regional observations by remote-sensing satellites play an important role in mapping, disaster relief, and other fields. The efficiency of single-satellite observation is low, and multisatellite collaborative observation is the main means for rapidly observing large areas. To date, multisatellite collaborative observation is mostly studied on optical remote-sensing satellites, but research on the collaborative observation of SAR satellites is limited. Moreover, SAR satellites have imaging mechanisms and modes distinct from those of optical satellites, and thus the optical satellite collaborative planning method cannot be fully applied to SAR satellites. For the optimization of the performance of SAR multisatellite collaborative observation, research into SAR multisatellite collaborative regional observation technologies is crucial.First, the coverage calculation of large-scale complex areas was analyzed, and a complex area coverage calculation method that combines Gaussian projection, grid division, and geometric operations and can realize the coverage calculation of any complex area, was proposed. Then, an accurate coverage analysis was performed on the SAR strip imaging mode, and a candidate area decomposition method combining angle restriction and two-dimensional decomposition was established. Optimization efficiency was improved by reducing the optimization space through angle restriction, and complex continuous optimization problems were discretized through two-dimensional decomposition. These approaches allowed the use of genetic algorithms for optimization. Finally, an improved genetic algorithm combining greedy algorithm initialization, elite retention strategy, and cubic fitness function was formulated for regional coverage optimization. Chromosome encoding, crossover, and mutation operations were designed for optimization, an optimal retention strategy was used to improve optimization speed and stability, and the cubic fitness function was used to improve the optimization effect.This study selected four on-orbit SAR satellites, namely, GF-3 01, GF-3 02, GF-3 03, and HISEA-1, and three regional targets, namely, Beijing, Tianjin, and Shanghai, for simulation experiments. The simulation time was 5 days, and the orbit data included real TLE data. The SGP4 orbit propagation model was used, and the beam parameters of the SAR satellite were reasonably simulated. Experimental results show that the coverage rates of the proposed method on three regional targets in Beijing, Tianjin, and Shanghai increased by 3.17%, 2.94%, and 9.02% compared with the coverage rates obtained with the greedy algorithm. In fine grids, the coverage result of the proposed method in the Shanghai area was 7.3% higher than that of the greedy algorithm.This study analyzes the SAR multisatellite collaborative complex area coverage planning technology, constructs a feasible SAR multisatellite collaborative complex area observation planning process, and proposes a complex area coverage planning method suitable for the SAR multisatellite strip imaging mode. This algorithm can provide a technical basis for the establishment of a SAR multisatellite collaborative regional planning system. However, the proposed method simplifies constraints at the imaging signal processing level of SAR satellites, and subsequent research will conduct in-depth research on the characteristics of SAR satellite imaging signal processing.

palabra clave

remote sensing;spaceborne SAR;multi-satellite collaboration;regional observation;coverage calculation;regional decomposition;genetic algorithm

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