Abstract
Millimeter-wave sensing using automotive radar imposes high requirements on the applied signal processing in order to obtain the necessary resolution for current imaging radar. High-resolution direction of arrival estimation is needed to achieve the desired spatial resolution, limited by the total antenna array aperture. This work gives an overview of the recent progress and work in the field of deep learning based direction of arrival estimation in the automotive radar context, i.e. using only a single measurement snapshot. Additionally, several deep learning models are compared and investigated with respect to their suitability for automotive angle estimation. The models are trained with model- and data-based approaches for data generation, including simulated scenarios as well as real measurement data from more than 400 automotive radar sensors. Finally, their performance is compared to several baseline angle estimation algorithms like the maximum-likelihood estimator. All results are discussed with respect to the estimation error, the resolution of closely spaced targets and the total estimation accuracy. The overall results demonstrate the viability and advantages of the proposed data generation methods, as well as super-resolution capabilities of several architectures.
Highlights
Automotive radar is a key sensor for reliable detection of obstacles and road users
Many advantages like small form factor, low cost compared to e.g. lidar, good robustness towards poor weather conditions, as well as accurate range, velocity and angle estimation, make radar the preferred choice for many advanced driver assistant systems (ADAS) functions like adaptive cruise control (ACC) or lane change assist (LCA) [3], [4]
The direction of arrival (DOA) estimation methods described in this paper are not limited to a particular radar waveform, in this chapter we establish a relation to fast-chirp or chirp-sequence frequency-modulated continuous-wave (FMCW) radar, the predominant radar waveform used in the automotive domain today [4]
Summary
Automotive radar is a key sensor for reliable detection of obstacles and road users. It finds wide application in advanced driver assistant systems (ADAS) [1] and is considered as one key sensing technology considered for highly-automated driving (HAD) [2]. The physical angle (azimuth or elevation) of a target with respect to the radar sensor is estimated by appropriate direction of arrival (DOA) estimation techniques. The radar compares TX and RX signals, from which the basic parameters of the sensing task can be estimated: number of targets Ktgt as well as range rktgt , radial velocity vr,ktgt , azimuth φk , and elevation k for each target ktgt This collection of targets detected in each measurement cycle is referred to as the target list and it can be interpreted as a pointcloud obtained from radar measurements. An important part of this work is to give an overview on how deep learning can be used to generate target lists with high resolution in azimuth and elevation, i.e. how the DOA estimation step can be implemented using DL approaches. The basic processing steps in automotive radar and the involved data models are outlined
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