A Scheduling Method of Using Multiple SAR Satellites to Observe a Large Area

Area of Science:

• Remote Sensing
• Operations Research
• Computer Science

Background:

• Scheduling multiple synthetic aperture radar (SAR) satellites for large irregular area (SMA) observation presents a complex nonlinear combinatorial optimization challenge.
• The solution space for SMA grows exponentially with area size, making traditional methods inefficient.
• Maximizing profit from acquired observational data is a key objective in satellite tasking.

Purpose of the Study:

• To develop an efficient method for solving the synthetic aperture radar (SAR) satellite scheduling problem for large irregular areas (SMA).
• To maximize the total profit obtained from observing target areas with multiple SAR satellites.
• To present a novel three-phase approach for optimal satellite scheduling.

Main Methods:

• A three-phase method: grid space construction, candidate strip generation, and strip selection.
• Grid space construction discretizes irregular areas and calculates potential profit.
• Candidate strip generation produces potential observation paths, followed by strip selection using a tabu search algorithm with variable neighborhoods to determine the optimal schedule.

Main Results:

• The proposed method successfully schedules multiple SAR satellites for SMA observation.
• Simulation experiments demonstrate the effectiveness of the developed algorithms.
• The novel method achieved a 6.38% profit improvement compared to the best existing methods under identical resource constraints.

Conclusions:

• The proposed three-phase method provides an effective solution for the complex SAR satellite scheduling problem for large irregular areas.
• The integration of normalized grid space construction, candidate strip generation, and a variable neighborhood tabu search algorithm optimizes scheduling for maximum profit.
• This approach offers a significant improvement in observational efficiency and profit generation for satellite missions.