Low-Complexity High-Resolution Parameter Estimation for Automotive MIMO Radars

Lin, Yu; Lee, Ta-Sung; Pan, Yun Han; Lin, Kuan Heng

doi:10.1109/access.2019.2926413

Cited by 30 publications

(18 citation statements)

References 31 publications

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“…The right-tilted angle information is due to the frequency offsets of subcarriers for every TX as described in (27). Since the range-angle coupling errors are closely related to the unambiguous range, an OFDM radar with the spectrally interleaved MIMO is not so good to operate a large number of TX antennas.…”

Section: B Simulation Resultsmentioning

confidence: 99%

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Time-and-Frequency Hybrid Multiplexing for Flexible Ambiguity Controls of DFT-coded MIMO OFDM Radar

Suh

Lee²,

Gil

et al. 2021

IEEE Access

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A time-and-frequency hybrid multiplexing technique for a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) radar is proposed. Discrete Fourier transform (DFT)-coded OFDM waveforms are introduced, which allow it to be free from range-dependent angle errors. These are readily used to provide both time-domain and frequency-domain multiplexings in the MIMO OFDM radar. The DFT-coded frequency-domain multiplexing shortens the maximum unambiguous range, while the DFT-coded time-domain multiplexing lowers the maximum Doppler ambiguity. A hybrid of both domain multiplexing techniques can mitigate the respective limitations by adaptively selecting the proper DFT matrix size in each multiplexing domain. This allows to solve the intrinsic range and Doppler ambiguity problems of MIMO radars by controlling the hybrid ratio of the two kinds of DFT-code based multiplexing methods. Range-Doppler and range-angle maps of four examples with different hybrid multiplexing ratios are simulated with a MIMO OFDM radar numerical platform.INDEX TERMS Discrete Fourier transform (DFT) code, hybrid multiplexing, multiple-input multipleoutput (MIMO) radar, orthogonal frequency-division multiplexing (OFDM) radar, range and Doppler ambiguities.

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Section: B Simulation Resultsmentioning

confidence: 99%

“…12 for the case of the spectrally interleaved MIMO. The angle error of each target shows the dependency of the range ambiguity as described in (27).…”

Section: B Simulation Resultsmentioning

confidence: 99%

Time-and-Frequency Hybrid Multiplexing for Flexible Ambiguity Controls of DFT-coded MIMO OFDM Radar

Suh

Lee²,

Gil

et al. 2021

IEEE Access

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show abstract

“…or alternative approaches, such as coherent addition of the antenna channels [39]. The latter would correspond to a range-Doppler (RD) image obtained from RX beamforming, i.e.…”

Section: Target Detection Detection List and Range-doppler Resolutionmentioning

confidence: 99%

A Machine Learning Perspective on Automotive Radar Direction of Arrival Estimation

et al. 2022

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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 databased 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 superresolution capabilities of several architectures.

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“…In a similar way, the amplitude attenuation of the reflected signal reaching each receiving array is

. Similarly, the echo received by the n array element is

where

is the Gaussian white noise received by the n array, and

represents receiving the steering vector [ 33 , 34 ].…”

Section: Mimo Radar Signal Processing Algorithmmentioning

confidence: 99%

MIMO Radar Parallel Simulation System Based on CPU/GPU Architecture

Liu

Yang

Qin

et al. 2022

Sensors

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The data volume and computation task of MIMO radar is huge; a very high-speed computation is necessary for its real-time processing. In this paper, we mainly study the time division MIMO radar signal processing flow, propose an improved MIMO radar signal processing algorithm, raising the MIMO radar algorithm processing speed combined with the previous algorithms, and, on this basis, a parallel simulation system for the MIMO radar based on the CPU/GPU architecture is proposed. The outer layer of the framework is coarse-grained with OpenMP for acceleration on the CPU, and the inner layer of fine-grained data processing is accelerated on the GPU. Its performance is significantly faster than the serial computing equipment, and satisfactory acceleration effects have been achieved in the CPU/GPU architecture simulation. The experimental results show that the MIMO radar parallel simulation system with CPU/GPU architecture greatly improves the computing power of the CPU-based method. Compared with the serial sequential CPU method, GPU simulation achieves a speedup of 130 times. In addition, the MIMO radar signal processing parallel simulation system based on the CPU/GPU architecture has a performance improvement of 13%, compared to the GPU-only method.

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Low-Complexity High-Resolution Parameter Estimation for Automotive MIMO Radars

Cited by 30 publications

References 31 publications

Time-and-Frequency Hybrid Multiplexing for Flexible Ambiguity Controls of DFT-coded MIMO OFDM Radar

Time-and-Frequency Hybrid Multiplexing for Flexible Ambiguity Controls of DFT-coded MIMO OFDM Radar

A Machine Learning Perspective on Automotive Radar Direction of Arrival Estimation

MIMO Radar Parallel Simulation System Based on CPU/GPU Architecture

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