Height estimation for automotive MIMO radar with group-sparse reconstruction

TL;DR

This paper introduces a novel group-sparse reconstruction method for accurate height and azimuth estimation of distant small objects using automotive MIMO radar, accounting for complex multipath ground reflections.

Contribution

It develops a new sequential estimation approach leveraging group-sparsity, suitable for flat-road scenarios and incoherent sensors, improving accuracy and reducing false alarms.

Findings

Significantly increased estimation accuracy in simulations

Reduced false alarms for small object detection

Effective height estimation with block orthogonal matching pursuit

Abstract

A method is developed for sequential azimuth and height estimation of small objects at far distances in front of a moving vehicle using coherent or mutually incoherent MIMO arrays. The model considers phases and amplitudes for near-field multipath signals produced by specular non-diffusive ground-reflections where the reflection phase shift and power attenuation due to the interaction with the ground is assumed unknown. Group-sparsity allows combining measurements along the trajectory of the vehicle provided that the road is flat as well as measurements from multiple incoherent sensors at different locations in the vehicle. It is shown in simulations that the proposed approach significantly increases estimation accuracy and decreases false alarms, both crucial for the detection of small objects at far distances. This model is suitable for non-uniform sparse arrays and can be used for height estimation using efficient methods such as block orthogonal matching pursuit.