Multi-stage Planning for Multi-target Surveillance using Aircrafts Equipped with Synthetic Aperture Radars Aware of Target Visibility

TL;DR

This paper introduces a multi-stage planning system for SAR-equipped aircraft that optimizes multi-target trajectories considering terrain and visibility, using neural networks and trajectory optimization.

Contribution

It presents a novel multi-stage approach combining neural network predictions and optimization for terrain-aware, multi-target SAR aircraft trajectory planning.

Findings

System ensures high-quality multi-target SAR imaging

Approach is robust and suitable for real-time operations

Neural network effectively predicts visibility segments

Abstract

Generating trajectories for synthetic aperture radar (SAR)-equipped aircraft poses significant challenges due to terrain constraints, and the need for straight-flight segments to ensure high-quality imaging. Related works usually focus on trajectory optimization for predefined straight-flight segments that do not adapt to the target visibility, which depends on the 3D terrain and aircraft orientation. In addition, this assumption does not scale well for the multi-target problem, where multiple straight-flight segments that maximize target visibility must be defined for real-time operations. For this purpose, this paper presents a multi-stage planning system. First, the waypoint sequencing to visit all the targets is estimated. Second, straight-flight segments maximizing target visibility according to the 3D terrain are predicted using a novel neural network trained with deep reinforcement learning. Finally, the segments are connected to create a trajectory via optimization that imposes 3D Dubins curves. Evaluations demonstrate the robustness of the system for SAR missions since it ensures high-quality multi-target SAR image acquisition aware of 3D terrain and target visibility, and real-time performance.