Decoupling the Doppler Ambiguity Interval From the Maximum Operational Range and Range-Resolution in FMCW Radars

Neemat, Sharef; Krasnov, Oleg A.; Zwan, Fred van der; Yarovoy, Alexander

doi:10.1109/jsen.2020.2972152

Cited by 15 publications

(3 citation statements)

References 16 publications

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“…Equation (14) shows that the range resolution depends entirely on the bandwidth and has nothing to do with the frequency resolution [19,20]. In theory, increasing the bandwidth can improve the distance resolution, thereby improving the ranging accuracy.…”

Section: Theory Of Ranging Performance Improvement Based On Frequency...mentioning

confidence: 99%

Research on wingtip distance measurement of high-speed coaxial helicopter based on 77 GHz FMCW millimeter-wave radar

Zhang¹,

Qiu²,

Hu³

et al. 2023

Meas. Sci. Technol.

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Aiming at the requirements of short-range, high-precision, and high-speed measurement of coaxial helicopter wingtip distance measurement, a millimeter-wave radar wingtip distance measurement scheme is proposed, and the ranging accuracy of FMCW millimeter-wave radar is improved by optimizing the frequency estimation method and the beat-frequency signal processing process. The multi-antenna data coherent superposition method and the correlation search for the estimated frequency are used to shorten the measurement period, and at the same time, the ranging accuracy of the millimeter-wave radar can be improved, and the single measurement time can be guaranteed. Based on frequency refinement, frequency offset method, phase method, amplitude method and fitted approximation method, the frequency estimation of millimeter wave radar differential frequency signal is combined with the influence of bandwidth, measured target shape and material on ranging to improve millimeter wave radar ranging performance comprehensively. Through simulations and experiments, the accuracy, stability, time consuming and application conditions of the optimized algorithm are comprehensively evaluated, and the feasibility of millimeter wave radar wingtip ranging is verified

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Section: Theory Of Ranging Performance Improvement Based On Frequency...mentioning

confidence: 99%

Research on wingtip distance measurement of high-speed coaxial helicopter based on 77 GHz FMCW millimeter-wave radar

Zhang¹,

Qiu²,

Hu³

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The mapping of peak k det indices l kdet , m kdet to its corresponding physical target parameters such as r ktgt and v r,ktgt is given by ( 14) as well as ( 6) and (7). Peak interpolation methods [40] allow for a finer accuracy of λ, ζ and hence r, v r compared to what is given by the DFT grid resolution, and strategies exist to resolve ambiguities occurring if target range and/or speed exceed the unambiguous interval [41]. Without further elaboration on the peak search strategy pks(•) details, common methods in automotive radar are typically based on variants of constant false alarm rate (CFAR) detection principles, see [3], [8] and references therein.…”

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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“…6). Furthermore, the proposed E2E architecture can compensate for phenomena, such as range migration or phase wrapping, which can occur in high-speed maneuvering ego-platform situations [36]. The network can be decomposed into three parts: the initial feature extraction (IFE), deep feature extraction (DFE), and state estimation.…”

Section: Network Architecturementioning

confidence: 99%

Complex-Valued Channel Attention and Application in Ego-Velocity Estimation With Automotive Radar

et al. 2021

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Attention mechanisms have been widely integrated with various neural networks to boost performance. However, when an attention mechanism was applied to a radar ego-velocity estimation network, the importance of carefully handling the amplitude and phase of complex-valued tensor was revealed. Therefore, in this study, we present a self-attention mechanism designed to handle complexvalued tensors in order to capture the rich contextual relationships implied within amplitude and phase. To exploit the advantages of complex-valued attention (CA), we evaluated its impact while performing egovelocity estimation tasks based on radar data, whose amplitude and phase are related to the electromagnetic scattering of the target being observed. Radars are suitable sensors for such tasks as they are capable of long-range detection and instantaneous velocity measurement under variable weather and lighting conditions. In particular, we coupled our CA module with complex-valued neural networks, known to be particularly powerful for handling wave phenomena. The proposed method exhibits robust estimation performance, regardless of whether the Doppler ambiguity problem occurs and eliminates the dependence on preprocessing stages, including target detection and static target indication. Furthermore, it achieves improved stability during training via geometrical constraint regularization, and implicitly allows velocity conversion between the sensor and vehicle frames, even if the mount information of the sensor was not provided. Finally, ablation experiments conducted on extensive real-world datasets show noticeable improvement in estimation performance. INDEX TERMS Automotive radar; complex-valued attention; complex-valued neural network; deep learning; ego-velocity estimation This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Decoupling the Doppler Ambiguity Interval From the Maximum Operational Range and Range-Resolution in FMCW Radars

Cited by 15 publications

References 16 publications

Research on wingtip distance measurement of high-speed coaxial helicopter based on 77 GHz FMCW millimeter-wave radar

Research on wingtip distance measurement of high-speed coaxial helicopter based on 77 GHz FMCW millimeter-wave radar

A Machine Learning Perspective on Automotive Radar Direction of Arrival Estimation

Complex-Valued Channel Attention and Application in Ego-Velocity Estimation With Automotive Radar

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