DeepEgo: Deep Instantaneous Ego-Motion Estimation Using Automotive Radar

The problem of instantaneous ego-motion estimation with mm-wave automotive radar is studied. DeepEgo, a deep learning-based method, is proposed for achieving robust and accurate ego-motion estimation. A hybrid approach that uses neural networks to extract complex features from input point clouds and applies weighted least squares (WLS) for...

Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars

A novel framework to enhance the angular resolution of automotive radars is proposed. An approach to enlarge the antenna aperture using artificial neural networks is developed using a self-supervised learning scheme. Data from a high angular resolution radar, i.e., a radar with a large antenna aperture, is used to train a deep neural network...

Vehicular Motion-based DOA Estimation with a Limited Amount of Snapshots for Automotive MIMO Radar

The problem of high-resolution direction-of-arrival (DOA) estimation based on a limited amount of snapshots in automotive multiple-input multiple-output (MIMO) radar has been studied. The number of snapshots is restricted to minimize target spread/migration in range and/or Doppler domains. A computationally efficient approach for side-looking...

Fusion of Data from Multiple Automotive Radars for High-Resolution DoA Estimation

High angular resolution is in high demand in automotive radar. To achieve a high azimuth resolution a large aperture antenna array is required. Although MIMO technique can be used to form larger virtual apertures, a large number of transmitter-receiver channels are needed, which is still technologically challenging and costly. To circumvent this...

An Approach for High-Angular Resolution Implementation in Moving Automotive MIMO Radar

A method exploiting the movement of the vehicle to boost the cross-range resolution of automotive radar by forming a larger virtual array is proposed. Initial simulated results show that the proposed method with the traditional Digital beamforming (DBF) algorithm can separate targets that cannot be otherwise recognized by the traditional MIMO...

Enhancing Angular Resolution Using Neural Networks in Automotive Radars

Poor angular resolution is one of the main disadvantages of automotive radars, and the reason why lidar technology is widely used in the automotive industry. For a fixed frequency, the angular resolution of a conventional Multiple-Input Multiple-Output (MIMO) radar is limited by the number of physical antennas, and therefore improve the...

Automotive radar interference study for different radar waveform types

Mutual interference between different radar waveforms used in automotive radar applications is studied. The existing interference analysis is extended to a generalised radar-to-radar interference equation that covers most of the common interference scenarios for automotive radar systems. The outcome of the generalised equation is demonstrated...

Target Localization Using MIMO-Monopulse: Application on 79 GHz FMCW Automotive Radar

A novel target azimuth estimation algorithm called ”MIMO-Monopulse” is proposed by combining monopulse approach with multiple-input multiple-output (MIMO) radar. Chebyshev and Zolotarev weighting are applied to synthesis sum and difference pattern of MIMO-monopulse. A new visualization method for monopulse ratio is discussed. Finally, the...