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

2026-04-18

AI Summary

This work addresses the challenge of real-time trajectory generation for synthetic aperture radar (SAR) platforms operating over complex 3D terrain, where flight paths must simultaneously ensure high-quality imaging and visibility of multiple targets. The authors propose a three-stage planning framework: first, determining an optimal visitation sequence for target waypoints; second, employing a deep reinforcement learning–trained neural network to predict straight-line flight segments that maximize cumulative target visibility while accounting for 3D terrain occlusions; and third, smoothly connecting these segments into a feasible full trajectory using 3D Dubins curves. This approach represents the first integration of target visibility modeling with 3D terrain constraints, combining deep reinforcement learning with geometric path planning to achieve scalable, robust, and real-time trajectory synthesis without compromising multi-target SAR image quality.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• multi-target surveillance
• synthetic aperture radar
• trajectory planning
• target visibility
• 3D terrain

Innovation

• multi-stage planning
• synthetic aperture radar (SAR)
• target visibility
• deep reinforcement learning
• 3D Dubins curves