Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain

TL;DR

This paper explores processing radar data in the frequency domain to enhance robustness and extract comprehensive scene information for multi-object tracking on fast-moving vehicles.

Contribution

It introduces a frequency domain processing approach for radar data, demonstrating improved robustness and scene understanding in high-dynamics scenarios.

Findings

Frequency domain processing improves robustness against noise and structural errors.

Fourier SOFT in 2D successfully demonstrates radar-only odometry.

Initial experiments on Boreas dataset validate the approach.

Abstract

We promote in this paper the processing of radar data in the frequency domain to achieve higher robustness against noise and structural errors, especially in comparison to feature-based methods. This holds also for high dynamics in the scene, i.e., ego-motion of the vehicle with the sensor plus the presence of an unknown number of other moving objects. In addition to the high robustness, the processing in the frequency domain has the so far neglected advantage that the underlying correlation based methods used for, e.g., registration, provide information about all moving structures in the scene. A typical automotive application case is overtaking maneuvers, which in the context of autonomous racing are used here as a motivating example. Initial experiments and results with Fourier SOFT in 2D (FS2D) are presented that use the Boreas dataset to demonstrate radar-only-odometry, i.e., radar-odometry without sensor-fusion, to support our arguments.