Super-resolution Doppler beam sharpening method using fast iterative adaptive approach-based spectral estimation

Mao, Deqing; Zhang, Yin; Huang, Yulin; Yang, Jianyu

doi:10.1117/1.jrs.12.015020

Cited by 25 publications

(4 citation statements)

References 24 publications

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“…The PRI was set as large as possible within the radar limitations to have its best Doppler resolution at the cost of poor range resolution. Because the movement of the radar is limited, the improvement in the performance of the DBS over DBF is not shown here, but this has been demonstrated in [21], [24], [27], [28], and [29]. The experiment results for the three corner reflectors with different processing methods are shown in Fig.…”

Section: Resultsmentioning

confidence: 96%

“…Daniel et al [21] investigated the application of DBS for the angular resolution refinement of low-Terahertz radar sensing. Mao et al [24] combined DBS with the fast IAA to achieve high azimuth resolution in the forward-squint region. It is challenging to use this approach in the forward-looking direction, which is, especially, of interest for autonomous vehicles, because of two challenging problems.…”

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. The proposed method does not require any prior information on the environment, the number of targets, and their locations.…”

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confidence: 99%

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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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Section: Resultsmentioning

confidence: 96%

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confidence: 99%

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confidence: 99%

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

et al. 2022

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“…In large squint-looking direction, the range history of target P can be expanded by Taylor series. Because of the short dwelling time of scanning beam, the range history can be further approximately simplified as [20], [21]…”

Section: Problem Formulationmentioning

confidence: 99%

Doppler Beam Sharpening Using Estimated Doppler Centroid Based on Edge Detection and Fitting

Mao¹,

Zhang²,

Huang³

et al. 2019

IEEE Access

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Doppler beam sharpening (DBS) technology is widely used in applications, such as helicopter rescue and early warning surveillance. To obtain the desired DBS images with high quality, accurate Doppler centroid estimation (DoCE) is necessary. Conventional methods for Doppler centroid estimation based on navigational devices are sensitive to the errors of the measured motion parameters. Hence, several alternative data-depended approaches have been developed to reduce the error. In this paper, a novel data-depended Doppler centroid estimation method is proposed to improve the image quality of DBS. We begin the method by analyzing the characteristics of range-Doppler distribution in different regions of interests. Then, the edge feature of range-Doppler distribution in forward-looking direction is extracted using morphological filtering and edge detection methods. We will show that the edge feature defines the required Doppler centroid parameters, which can be utilized to estimate the Doppler centroids of the full scene. At last, the estimation error is reduced through fitting the edge with the minimum mean square error (MMSE) algorithm. As compared with conventional Doppler centroid estimation methods, the proposed method can significantly provide reliable estimation accuracy under low echo signal to noise ratio, independent of conditions that strictly required by conventional methods. Simulations and experiments verify the proposed method.INDEX TERMS Doppler beam sharpening, Doppler centroid estimation, edge detection and fitting.

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Super-resolution Doppler Beam Sharpening based on Online Tikhonov Regularization

Kang

Mao

Tuo

et al. 2019

2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)

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Super-resolution Doppler beam sharpening method using fast iterative adaptive approach-based spectral estimation

Cited by 25 publications

References 24 publications

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

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

Doppler Beam Sharpening Using Estimated Doppler Centroid Based on Edge Detection and Fitting

Super-resolution Doppler Beam Sharpening based on Online Tikhonov Regularization

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