UAV-SAR: Simultaneous Radar-Based Odometry and Synthetic-Array Sensing for Unmanned Aerial Vehicles

Abstract

Unmanned aerial vehicles (UAVs) require accurate odometry—i.e., estimating the position and velocity of the vehicle over time—as well as high-resolution sensing to safely and effectively operate in complex environments. Traditionally, GPS, cameras, and/or lidar sensors have been used to perform these functions. However, GPS can be jammed in contested environments while cameras and lidars fail in visually degraded conditions, limiting UAV operations in these scenarios. In this work, we present UAV-SAR, a unified architecture that utilizes mmWave radars to simultaneously achieve precise odometry measurements and perform high-resolution synthetic-array sensing. Here, UAV-SAR measures a UAV’s altitude and velocity from downward- and outward-facing radars and fuses these measurements within a commercially available flight controller to produce accurate odometry estimates. These odometry estimates are then used to dynamically construct synthetic arrays by coherently integrating multiple radar frames together over a duration of 0.5 s, improving the angular resolution by an order of magnitude compared to the physical array alone. Finally, a lightweight deep learning model is utilized to convert high-resolution range-angle responses into 2D point clouds suitable for downstream perception tasks. UAV-SAR is validated on a custom UAV prototype where it is integrated with ROS2 and the PX4 autopilot to demonstrate stable flight, reliable odometry, and high-resolution radar sensing in indoor environments.