T-FFTRadNet: Object Detection with Swin Vision Transformers from Raw ADC Radar Signals

TL;DR

This paper introduces T-FFTRadNet, a novel deep learning approach using hierarchical Swin Vision Transformers to detect objects directly from raw radar signals, reducing pre-processing and improving detection performance across various radar configurations.

Contribution

The paper proposes a new method that operates on raw radar ADC data using transformer-based models, eliminating the need for extensive Fourier Transform pre-processing.

Findings

Operates effectively on raw ADC radar signals.

Achieves comparable or better results than state-of-the-art methods.

Works across different radar configurations with fewer pre-processing steps.

Abstract

Object detection utilizing Frequency Modulated Continous Wave radar is becoming increasingly popular in the field of autonomous systems. Radar does not possess the same drawbacks seen by other emission-based sensors such as LiDAR, primarily the degradation or loss of return signals due to weather conditions such as rain or snow. However, radar does possess traits that make it unsuitable for standard emission-based deep learning representations such as point clouds. Radar point clouds tend to be sparse and therefore information extraction is not efficient. To overcome this, more traditional digital signal processing pipelines were adapted to form inputs residing directly in the frequency domain via Fast Fourier Transforms. Commonly, three transformations were used to form Range-Azimuth-Doppler cubes in which deep learning algorithms could perform object detection. This too has drawbacks, namely the pre-processing costs associated with performing multiple Fourier Transforms and normalization. We explore the possibility of operating on raw radar inputs from analog to digital converters via the utilization of complex transformation layers. Moreover, we introduce hierarchical Swin Vision transformers to the field of radar object detection and show their capability to operate on inputs varying in pre-processing, along with different radar configurations, i.e. relatively low and high numbers of transmitters and receivers, while obtaining on par or better results than the state-of-the-art.