Coherent, super resolved radar beamforming using self-supervised learning
arXiv:2106.13085 · doi:10.1126/scirobotics.abk0431
Abstract
High resolution automotive radar sensors are required in order to meet the high bar of autonomous vehicles needs and regulations. However, current radar systems are limited in their angular resolution causing a technological gap. An industry and academic trend to improve angular resolution by increasing the number of physical channels, also increases system complexity, requires sensitive calibration processes, lowers robustness to hardware malfunctions and drives higher costs. We offer an alternative approach, named Radar signal Reconstruction using Self Supervision (R2-S2), which significantly improves the angular resolution of a given radar array without increasing the number of physical channels. R2-S2 is a family of algorithms which use a Deep Neural Network (DNN) with complex range-Doppler radar data as input and trained in a self-supervised method using a loss function which operates in multiple data representation spaces. Improvement of 4x in angular resolution was demonstrated using a real-world dataset collected in urban and highway environments during clear and rainy weather conditions.
arXiv comment28 pages 10 figures
References in corpus (12)
- Attention U-Net: Learning Where to Look for the Pancreas
- Unsupervised Representation Learning by Predicting Image Rotations
- Spatial Compressive Sensing for MIMO Radar
- CNN based Road User Detection using the 3D Radar Cube
- Self-supervised Learning for ECG-based Emotion Recognition
- Self-Supervised Intrinsic Image Decomposition
- SPICE: Self-supervised Pitch Estimation
- Unsupervised Object Segmentation by Redrawing
- Radar-based Feature Design and Multiclass Classification for Road User Recognition
- Self-supervised audio representation learning for mobile devices
- An Adversarial Super-Resolution Remedy for Radar Design Trade-offs
- Self-supervised Learning for Speech Enhancement