A Trajectory Estimation Method Based on Microwave Three-Point Ranging for Sparse 3D Radar Imaging

Abstract

What are the main findings?

A microwave three-point ranging scheme with three external reflective spheres uniquely determines the radar’s 3D position at each sample—even under random jitter—without any communications link.

The resulting trajectory accuracy enables high-fidelity 3D radar imaging under sparse sampling.

What is the implication of the main finding?

A simple, retrofit-friendly external calibration (three spheres) increases the robustness and flexibility of sparse near-field radar imaging.

The method scales to UAV-borne 3D imaging, supporting accurate in-flight radar localization without relying on communications.

Precise estimate of antenna location is essential for high-quality three-dimensional (3D) radar imaging, especially under sparse sampling schemes. In scenarios involving synchronized scanning and rotational motion, small deviations in the radar’s transmitting position can lead to significant phase errors, thereby degrading image fidelity or even causing image failure. To address this challenge, we propose a novel trajectory estimation method based on microwave three-point ranging. The method utilizes three fixed microwave-reflective calibration spheres positioned outside the imaging scene. By measuring the one-dimensional radial distances between the radar and each of the three spheres, and geometrically constructing three intersecting spheres in space, the radar’s spatial position can be uniquely determined at each sampling moment. This external reference-based localization scheme significantly reduces positioning errors without requiring precise synchronization control between scanning and rotation. Furthermore, the proposed approach enhances the robustness and flexibility of sparse sampling strategies in near-field radar imaging. Beyond ground-based setups, the method also holds promise for drone-borne 3D imaging applications, enabling accurate localization of onboard radar systems during flight. Simulation results and error analysis demonstrate that the proposed method improves trajectory accuracy and supports high-fidelity 3D reconstruction under non-ideal sampling conditions.