Single snapshot DOA estimation

Abstract

Abstract. In array signal processing, direction of arrival (DOA) estimation has been studied for decades. Many algorithms have been proposed and their performance has been studied thoroughly. Yet, most of these works are focused on the asymptotic case of a large number of snapshots. In automotive radar applications like driver assistance systems, however, only a small number of snapshots of the radar sensor array or, in the worst case, a single snapshot is available for DOA estimation.In this paper, we investigate and compare different DOA estimators with respect to their single snapshot performance. The main focus is on the estimation accuracy and the angular resolution in multi-target scenarios including difficult situations like correlated targets and large target power differences. We will show that some algorithms lose their ability to resolve targets or do not work properly at all. Other sophisticated algorithms do not show a superior performance as expected. It turns out that the deterministic maximum likelihood estimator is a good choice under these hard conditions.

A closed-form phase-comparison ML DOA estimator for automotive radar with one single snapshot

“…All targets are in the far field of the receive array with the azimuth angle denoted by . In the scenario where the radar system demands high resolution specifications for safety reasons, it is difficult to collect more than one snapshot for angle estimation due to that the velocity resolution is inversely proportional to the observation interval [3,5]. Therefore, the main challenge to the proposed estimator is to obtain precise angle information for each detected target with a single snapshot.…”

“…One of the key performance limitations of today's radar techniques is the azimuth resolution [1]. Various direction-of-arrival (DOA) estimation methods are available in the literatures [2,3,4]. The super-resolution approach has received considerable attention as a possible solution, which utilizes eigenspace decomposition and sub-space projection techniques to deliver high resolution estimation.…”

“…Its major drawbacks include high computational cost and the requirement for more than one snapshot for constructing the correlation matrix. These pose the disadvantage to practical real-time embedded systems [3]. As an alternative, amplitude-comparison monopulse technique requires lower complexity but its performance is sensitive to inhomogeneous antenna patterns caused by manufacture processes [4].…”

Radar Sensors for Autonomous Driving: Modulation Schemes and Interference Mitigation

“…Different algorithms exist to perform the DoA estimation based on the different received signals [26], for example in the Bartlett beamformer, subspace methods [27], or maximum likelihood approaches [28]. If the virtual antenna elements are equally spaced, the Bartlett beamformer can be interpreted as a simple Fourier transform.…”

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

“…In Chapter 2, we discussed the challenge of using a super-resolution algorithm for target localization in FMCW automotive radar application. One of the main advantages of MIMO-monopulse is that the DOA of targets can be estimated using only single pulse, which is called single snapshot in literature [56]. should be applied to a matrix with M N × L dimension, which is computationally expensive for realization on a real automotive radar system.…”