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

Yuan, Sen; Fioranelli, Francesco; Yarovoy, Alexander

doi:10.23919/eurad50154.2022.9784507

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…Precise DoA estimation is crucial for smart antennas in MIMO systems, playing a pivotal role in achieving spatial signal processing, beamforming, and link reliability. As a consequence, applications reliant on precise DoA estimation, e.g., vehicular communications [19], [20], can experience significant improvements and thrive in performance through the advancement of research in this domain.…”

Section: Introductionmentioning

confidence: 99%

A Novel Three-Dimensional Direction-of-Arrival Estimation Approach Using a Deep Convolutional Neural Network

Mylonakis,

Zaharis

2024

IEEE Open J. Veh. Technol.

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This paper aims to constitute a noteworthy contribution to the domain of direction-ofarrival (DoA) estimation through the application of deep learning algorithms. We approach the DoA estimation challenge as a binary classification task, employing a novel grid in the output layer and a deep convolutional neural network (DCNN) as the classifier. The input of the DCNN is the correlation matrix of signals received by a 4 × 4 uniformly spaced patch antenna array. The proposed model's performance is evaluated based on its capacity to predict angles of arrival from any direction in a three-dimensional space, encompassing azimuth angles within the interval [0 • , 360 • ) and polar angles within [0 • , 60 • ]. We aim to optimize the utilization of spatial information and create a robust, precise, and efficient DoA estimator. To address this, we conduct comprehensive testing in diverse scenarios, encompassing the simultaneous reception of multiple signals across a wide range of signal-to-noise ratio values. Both mean absolute error and root mean squared error are calculated to assess the performance of the DCNN. Rigorous comparison with conventional and state-of-the-art endeavors emphasizes the proposed model's efficacy.

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Section: Introductionmentioning

confidence: 99%

A Novel Three-Dimensional Direction-of-Arrival Estimation Approach Using a Deep Convolutional Neural Network

Mylonakis,

Zaharis

2024

IEEE Open J. Veh. Technol.

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“…Potential solutions could be adding more virtual array elements using the platform motion information, or developing a novel direction-finding framework to extract more information from the available limited elements. Using the motion information to form a synthetic virtual aperture on automotive MIMO radar for higher angular resolution is an established approach [7], [8], [9]. However, they require accurate radar position information, enough movement in the antenna directions, and will be generally valid only for side-looking radar.…”

Section: Introductionmentioning

confidence: 99%

Improved Direction Finding Accuracy for A Limited Number of Antenna Elements with Harmonic Characteristic Analysis

Yuan

Fioranelli

Yarovoy

2022

2022 19th European Radar Conference (EuRAD)

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A direction-finding approach for arrays with a limited number of antenna elements has been investigated. A method based on the harmonic analysis of the received signal has been proposed to solve it. The angle estimation accuracy has been improved by angle searching and peak detection. The proposed method is theoretically described and numerical simulations are provided to verify its effectiveness. Compared with classical direction-finding methods with limited antenna elements, significant improvements have been demonstrated.

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“…The work in [35] uses the residual motion compensation to improve the SAR image quality for automotive. The velocity information was also used for wideband DOA estimation with compensation of range migration and the presence of Doppler ambiguity in [36] and for high angular resolution imaging in [37] and [38].…”

mentioning

confidence: 99%

“…However, all the aforementioned algorithms [32], [33], [34], [35], [36], [37], [38] do not consider the ambiguity problem in case of the forward-looking radar. To our best knowledge, compared with [21] and [24], this article is the first to solve the ambiguity in DBS for forward-looking MIMO radar.…”

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confidence: 99%

A Novel Approach to Unambiguous Doppler Beam Sharpening for Forward-Looking MIMO Radar

et al. 2022

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The ambiguity problem of targets in Doppler beam sharpening (DBS) with forward-looking radar is considered. While DBS is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the platform movement. To address this problem, an approach named unambiguous Doppler-based forwardlooking multiple-input multiple-output (MIMO) radar beam sharpening scan (UDFMBSC) is proposed, based on the combination of MIMO processing and DBS. The performance of the proposed method is compared to existing approaches using simulated data with point-like and extended targets. The method is successfully verified using experimental data. Index Terms-Beam scan, Doppler beam sharpening (DBS), forward-looking radar, multiple-input multiple-output (MIMO) radar processing. I. INTRODUCTION T HE success of autonomous vehicles strongly depends on their environment-sensing capabilities. Autonomous vehicles are equipped with multiple sensors, including radars, cameras, and lidars [1]. Among them, radar can provide accurate and direct measurements of the range, relative velocity, and angle of multiple targets; offer a long-range coverage of more than 200 m even in challenging weather or lighting conditions [2]; and be easily packaged behind the optically nontransparent fascia of the vehicle. Therefore, radar is typically used in current ADASs' tasks, such as adaptive cruise control (ACC) [3], forward collision avoidance (FCA) [4], lane-change assistance [5], or evasion assistance [6], and is considered one of the key technologies for next-generation autonomous vehicles [7].

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

Cited by 8 publications

References 12 publications

A Novel Three-Dimensional Direction-of-Arrival Estimation Approach Using a Deep Convolutional Neural Network

A Novel Three-Dimensional Direction-of-Arrival Estimation Approach Using a Deep Convolutional Neural Network

Improved Direction Finding Accuracy for A Limited Number of Antenna Elements with Harmonic Characteristic Analysis

A Novel Approach to Unambiguous Doppler Beam Sharpening for Forward-Looking MIMO Radar

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