Automotive Radar Interference Reduction Based on Sparse Bayesian Learning

Chen, Shengyi; Taghia, Jalal; Kühnau, Uwe; Fei, Tai; Grunhaupt, Frank; Martin, Rainer

doi:10.1109/radarconf2043947.2020.9266706

Cited by 12 publications

(4 citation statements)

References 12 publications

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“…As analyzed above, the range-Doppler processing is based on the narrow-band and slow-speed targets model, which is mismatched with the wide-band waveform and high-speed targets in modern civilian applications. Some existing works exploit the DFT matrix to construct the redundant dictionaries for RV map estimation with SBL techniques [21], [22]. Since their redundant dictionaries do not resolve this model mismatch problem, they suffer from the same drawbacks as the range-Doppler processing.…”

Section: Dictionary For Range-velocity Map Estimationmentioning

confidence: 99%

“…The amplitudes of all estimated RV maps are normalized to 1 for ease of comparison. For convenience, the RV map estimation methods, including the proposed method, the proposed fast method, the conventional range-Doppler processing, and the BSBL-based method [21] are denoted by SBL-RV, FSBL-RV, DFT-RV, and BSBL-RV, respectively. In addition, considering that the ℓ 1 regularization techniques are widely used for sparse representation recovery, we combine the LASSO method provided in [28] with the same dictionary used by SBL-RV and FSBL-RV for the RV map estimation and denote this method by LASSO-RV.…”

Section: Simulations and Experimentsmentioning

confidence: 99%

“…In RV map estimation, the SBL-based methods have been demonstrated to be superior to the conventional methods in terms of accuracy, resolution, and interference mitigation [21], [22], [23], [24]. A key issue in the sparse representation is to choose a suitable dictionary to decompose the signal.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High-Resolution and Accurate RV Map Estimation by Spare Bayesian Learning

Deng

Sun

et al. 2022

IEEE Trans. Veh. Technol.

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Section: Dictionary For Range-velocity Map Estimationmentioning

confidence: 99%

Section: Simulations and Experimentsmentioning

confidence: 99%

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High-Resolution and Accurate RV Map Estimation by Spare Bayesian Learning

Deng

Sun

et al. 2022

IEEE Trans. Veh. Technol.

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“…Several solutions have been proposed to face the problem of mutual interference. Most of the published studies focus on the attenuation or removal of the interference after it has been revealed at the receiver [7] [8], or by employing compressive sensing techniques [9]. Other approaches aim to mitigate or avoid the interference exploiting waveform diversity.…”

mentioning

confidence: 99%

Forward-Looking Passive Radar With Non-Uniform Linear Array for Automotive Applications

Blasone

Colone

Lombardo

2023

IEEE Trans. Veh. Technol.

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With the rapid and growing spread of automotive radar systems in modern vehicles, the problem of their mutual interference is becoming a major safety concern. This paper considers the use of receive-only sensors onboard the vehicles, which exploit an external illumination source to provide automotive radar functionalities. The passive radar paradigm would solve the problem of mutual interference, allowing different systems to share the same transmitted signal. A preliminary analysis of the feasibility of this concept is carried out, exploiting real-world transmitters of opportunity. The potentialities offered by both satellite and groundbased illuminators are investigated and the expected performance is evaluated in terms of achievable coverage and spatial resolution. Aiming to enable a practical implementation of the proposed concept, an appropriate signal processing scheme is proposed to obtain maps of the observed scene. A Doppler beam sharpening approach is adopted to discriminate and localize stationary scatterers in azimuth based on the differences in their relative Doppler shift. The problem of left/right ambiguity arising from the forward-looking geometry is tackled by exploiting an array of few antenna elements on receive. Specifically, an ambiguity removal approach is proposed, based on digital beam pattern adaptation, designed to maximise the response in the desired direction and suppress unwanted echoes from the corresponding ambiguous one. The effectiveness of this approach is further improved by cascading an apodization technique that prevents the undesirable increase in the noise level. Moreover, some criteria are introduced for the design of the antenna layout, resorting to non-uniform linear array configurations. The proposed system is tested against a simulated environment, where the multi-channel signal processing, combined with a properly designed array layout, is shown to provide an unambiguous mapping of the observed scene over wide angular sectors, even operating with few antenna elements on receive.

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Application of Variational Mode Decomposition to FMCW Radar Interference Mitigation

Balasooriya

Nallabolu

2022

Sensing Technology

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Automotive Radar Interference Reduction Based on Sparse Bayesian Learning

Cited by 12 publications

References 12 publications

High-Resolution and Accurate RV Map Estimation by Spare Bayesian Learning

High-Resolution and Accurate RV Map Estimation by Spare Bayesian Learning

Forward-Looking Passive Radar With Non-Uniform Linear Array for Automotive Applications

Application of Variational Mode Decomposition to FMCW Radar Interference Mitigation

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