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

arXiv: https://arxiv.org/abs/2604.16962 · HTML: https://mlsanity.com/paper/2604.16962/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 18 Apr 2026.

## Builds on (most cited first, 13 of 13)

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- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [MaxViT: Multi-Axis Vision Transformer](https://mlsanity.com/paper/2204.01697/) (2022)
- [Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement](https://mlsanity.com/paper/1810.09103/) (2018)
- [Hierarchical Width-Based Planning and Learning](https://mlsanity.com/paper/2101.06177/) (2021)
- [Trajectory and Resource Optimization for UAV Synthetic Aperture Radar](https://mlsanity.com/paper/2207.05891/) (2022)
- [Efficient Constraint Generation for Stochastic Shortest Path Problems](https://mlsanity.com/paper/2604.01855/) (2026)

## Abstract (arXiv)

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.