3D-Subspace-Based Auto-Paired Azimuth Angle, Elevation Angle, and Range Estimation for 24G FMCW Radar with an L-Shaped Array

Nam, HyungSoo; Li, Ying Chun; Choi, Byunggil; Oh, Daegun

doi:10.3390/s18041113

Cited by 7 publications

(11 citation statements)

References 28 publications

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“…It is found that the range and radial velocity of an object corresponds to the vertical and horizontal axis of the spectrum obtained by performing 2D FFT on a radar signal segment [9,10], which is usually referred to as the range-doppler spectrum. As shown for the ideal case in Fig.…”

Section: Preliminarymentioning

confidence: 99%

“…In this work, we attempt to fully exploit the FWMC radar signals to detect the presence and estimate the 3D positions of objects based on DNNs. It is known that for an FWMC radar with multiple antenna receivers, 3D information (i.e., range, elevation and azimuth) of interested objects is embedded in the received radar signals [9,10]. Our motivation for exploiting the DNN-based approach is that radar signals can be preprocessed and treated as images.…”

Section: Introductionmentioning

confidence: 99%

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Object Detection and 3d Estimation Via an FMCW Radar Using a Fully Convolutional Network

Zhang

Wenger

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper considers object detection and 3D estimation using an FMCW radar. The state-of-the-art deep learning framework is employed instead of using traditional signal processing. In preparing the radar training data, the ground truth of an object orientation in 3D space is provided by conducting image analysis, of which the images are obtained through a coupled camera to the radar device. To ensure successful training of a fully convolutional network (FCN), we propose a normalization method, which is found to be essential to be applied to the radar signal before feeding into the neural network. The system after proper training is able to first detect the presence of an object in an environment. If it does, the system then further produces an estimation of its 3D position. Experimental results show that the proposed system can be successfully trained and employed for detecting a car and further estimating its 3D position in a noisy environment.

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Section: Preliminarymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Object Detection and 3d Estimation Via an FMCW Radar Using a Fully Convolutional Network

Zhang

Wenger

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…In FMCW radar systems, to solve this problem of estimators based on FFT, subspace-based algorithms have been studied [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. In [ 19 , 20 ], Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT)-based algorithms are employed to accurately estimate the direction of arrival (DOA) of targets.…”

Section: Introductionmentioning

confidence: 99%

FMCW Radar Estimation Algorithm with High Resolution and Low Complexity Based on Reduced Search Area

Kim

Jin

Lee

et al. 2022

Sensors

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We propose a frequency-modulated continuous wave (FMCW) radar estimation algorithm with high resolution and low complexity. The fast Fourier transform (FFT)-based algorithms and multiple signal classification (MUSIC) algorithms are used as algorithms for estimating target parameters in the FMCW radar systems. FFT-based and MUSIC algorithms have tradeoff characteristics between resolution performance and complexity. While FFT-based algorithms have the advantage of very low complexity, they have the disadvantage of a low-resolution performance; that is, estimating multiple targets with similar parameters as a single target. On the other hand, subspace-based algorithms have the advantage of a high-resolution performance, but have a problem of very high complexity. In this paper, we propose an algorithm with reduced complexity, while achieving the high-resolution performance of the subspace-based algorithm by utilizing the advantages of the two algorithms; namely, the low-complexity advantage of FFT-based algorithms and the high-resolution performance of the MUSIC algorithms. The proposed algorithm first reduces the amount of data used as input to the subspace-based algorithm by using the estimation results obtained by FFT. Secondly, it significantly reduces the range of search regions considered for pseudo-spectrum calculations in the subspace-based algorithm. The simulation and experiment results show that the proposed algorithm achieves a similar performance compared with the conventional and low complexity MUSIC algorithms, despite its considerably lower complexity.

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“…Several studies on the algorithms of FMCW mmWave radar systems have been proposed [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Most of the proposed algorithms are based on Fast Fourier Transform (FFT), but it is difficult for FFT-based estimators to distinguish between two adjacent targets, such as ground and the water surface, because of the limitation of the bin width of the FFT spectrum given by

, in which

and

, respectively, denote the sampling frequency and the number of samples [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

“…Although its resolution is higher than that of conventional FFT-based estimation algorithms, it is still a few centimeters without much improvement [ 24 ]. Other algorithms [ 25 , 26 , 27 , 28 , 29 , 30 ], such as the multiple signal classification (MUSIC) [ 27 ], and estimation of signal parameters via rotational invariance technique (ESPRIT) algorithms [ 29 ] are also proposed. However, those super-resolution algorithms all add too much computing complexity, which is opposed to the conception of edge computing.…”

Section: Introductionmentioning

confidence: 99%

A Low-Power High-Accuracy Urban Waterlogging Depth Sensor Based on Millimeter-Wave FMCW Radar

Shui

Geng

et al. 2022

Sensors

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The method of making precise measurements of remote water depth using mmWave technology has great potential for preventing urban waterlogging. To achieve waterlogging prevention, the mmWave system needs to measure the water depth change accurately with a short acquisition time. This paper demonstrates a new accurate mmWave water depth measurement system based on Frequency Modulated Continuous Wave (FMCW) Radar with a center frequency of 77 GHz. To improve distance resolution and lower acquisition time, the Swept Frequency-Cross Correlation (SFCC) algorithm is proposed for the first time to improve the distance computation resolution by 9× and lower time complexity from O(n·logn) to O(n) compared to traditional FFT-based FMCW radar distance computation. A prototype system equipped with a humidity sensor, a processor module and TI’s FMCW radar module is designed for monitoring urban floods in cities. Using the prototype system with the proposed SFCC, the depth measurement error is reduced from 4.5 cm to less than 5 mm, compared to the default radar post-processing algorithm embedded in the radar module.

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3D-Subspace-Based Auto-Paired Azimuth Angle, Elevation Angle, and Range Estimation for 24G FMCW Radar with an L-Shaped Array

Cited by 7 publications

References 28 publications

Object Detection and 3d Estimation Via an FMCW Radar Using a Fully Convolutional Network

Object Detection and 3d Estimation Via an FMCW Radar Using a Fully Convolutional Network

FMCW Radar Estimation Algorithm with High Resolution and Low Complexity Based on Reduced Search Area

A Low-Power High-Accuracy Urban Waterlogging Depth Sensor Based on Millimeter-Wave FMCW Radar

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