Enhanced beamspace MUSIC for cost‐effective FMCW automotive radar

As one important sensor for automotive applications, frequency modulated continuous wave (FMCW) radars working at millimetre wave frequency, e.g. 24 or 77 GHz, are drawing much attention in recent years. Compared with range and radial velocity measurement, direction of arrival (DOA) estimation is more challenging in FMCW automotive radars due to the space limitation and computational complexity constraint. The feature of the signal processing procedure further results in the problem of single snapshot and relatively low signal‐to‐noise ratio (SNR) level. In this study, the authors propose a low‐complexity beamspace multiple signal classification (MUSIC) scheme to improve the DOA estimation performance, where (i) the prior information is exploited to improve beamformer design, (ii) a modified MUSIC estimator is formulated to alleviate the performance degradation due to low SNR, (iii) a joint sub‐matrix averaging and Toeplitz structure recovery processing is utilised to compensate the sample covariance estimation error resulted from single snapshot, especially for closely located targets. Simulations are given to demonstrate the efficiency of the authors’ proposed enhanced beamspace DOA estimation for cost‐effective FMCW automotive radar.