Coverage Path Planning For Multi-view SAR-UAV Observation System Under Energy Constraint

TL;DR

This paper addresses the complex problem of planning energy-efficient paths for multiple SAR-equipped UAVs to maximize coverage of target viewpoints, using clustering and optimization techniques.

Contribution

It introduces a novel approach combining ADPC clustering and PSO optimization for energy-aware coverage path planning in multi-view SAR-UAV systems.

Findings

The proposed method improves coverage efficiency.

It reduces energy consumption for UAV paths.

Experimental results validate the approach's effectiveness.

Abstract

Multi-view Synthetic Aperture Radar (SAR) imaging can effectively enhance the performance of tasks such as automatic target recognition and image information fusion. Unmanned aerial vehicles (UAVs) have the advantages of flexible deployment and cost reduction. A swarm of UAVs equipped with synthetic aperture radar imaging equipment is well suited to meet the functional requirements of multi-view synthetic aperture radar imaging missions. However, to provide optimal paths for SAR-UAVs from the base station to cover target viewpoints in the mission area is of NP-hard computational complexity. In this work, the coverage path planning problem for multi-view SAR-UAV observation systems is studied. First, the coordinate of observation viewpoints is calculated based on the location of targets and base station under a brief geometric model. Then, the exact problem formulation is modeled in order to fully describe the solution space and search for optimal paths that provide maximum coverage rate for SAR-UAVs. Finally, an Adaptive Density Peak Clustering (ADPC) method is proposed to overcome the additional energy consumption due to the viewpoints being far away from the base station. The Particle Swarm Optimization (PSO) algorithm is introduced for optimal path generation. Experimental results demonstrate the effectiveness and computational efficiency of the proposed approach.