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Interference Compression and Mitigation for Automotive FMCW Radar Systems
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0002-7464-5704
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0002-6643-312X
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0003-3707-2780
2022 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 22, no 20, p. 19739-19749Article in journal (Refereed) Published
Abstract [en]

Millimeter-wave (mm-wave) frequency-modu- lated continuous wave (FMCW) radars are increasingly being deployed for scenario perception in various applications. It is expected that the mutual interference between such radars will soon become a significant problem. Therefore, to maintain the reliability of the radar measurements, there must be procedures in place to mitigate this interference. This article proposes a novel interference mitigation technique that utilizes the pulse compression principle for interference compression and mitigation. The interference in the received time-domain signal is compressed using an estimated matched filter. Afterward, the compressed interference is discarded, and the signal is repaired in the pulse-compressed domain using an autoregressive (AR) model. Since the interference spans fewer samples after compression, the signal can be restored more accurately in the compressed domain. Real outdoor measurements show that the interference is effectively suppressed down to the noise floor using the proposed scheme. A signal to interference and noise ratio (SINR) gain of approximately 14 dB was achieved in the experimental data, supporting this study. Moreover, the results indicate that this method is also applicable to situations where multiple interference sources are present.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2022. Vol. 22, no 20, p. 19739-19749
Keywords [en]
Interference, Radar, Chirp, Sensors, Time-domain analysis, Time-frequency analysis, Radar detection, Automotive radar, interference cancellation, millimeter wave (mm-wave) radar, pulse compression, radar signal processing, signal reconstruction
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-23838DOI: 10.1109/JSEN.2022.3204505ISI: 000870341900066OAI: oai:DiVA.org:bth-23838DiVA, id: diva2:1708535
Note

open access

This work was supported in part by the Netport Science Park and in part by the Municipality of Karlshamn.

Available from: 2022-11-04 Created: 2022-11-04 Last updated: 2023-02-08Bibliographically approved
In thesis
1. Signal Processing Approaches for Interference Mitigation in Automotive Radar Systems
Open this publication in new window or tab >>Signal Processing Approaches for Interference Mitigation in Automotive Radar Systems
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Modern vehicles have several autonomous and semi-autonomous features, such as adaptive cruise control, lane keeping, adaptive headlights, and automatic emergency braking, ensuring a safe and comfortable driving experience. The vehicles typically rely on different sensors to "see" their surroundings and make decisions accordingly. Among these sensors, radar is particularly significant for its exceptional range and velocity estimation capabilities and plays an essential role in detecting and tracking objects within the vehicle's vicinity.

Since automotive radars operate in the same frequency range, there is a chance that radars operating in close proximity might encounter mutual interference. The interference can degrade the radar's performance and cause false alarms and missed detections, which can be particularly problematic in safety-oriented systems. This research aims to develop signal processing techniques to mitigate the interference effects in frequency-modulated continuous wave (FMCW) radars operating at 77-81 GHz and contribute to making modern radar applications safe and reliable. The interference mitigation methods investigated in this thesis fall into three categories: digital beamforming, time-domain signal reconstruction, and deep learning methods.

The digital beamforming approach utilizes the beam pattern of the receiving antenna array to mitigate interference by placing notches in the beam pattern. It is demonstrated that while this approach is applicable to MIMO radar systems, the notch resolution does not benefit from the extended virtual aperture. An adaptive digital beamforming approach based on the least mean squares (LMS) algorithm is also proposed to suppress interference in the received signal.

The time-domain signal reconstruction approaches aim to reconstruct the parts of the received baseband signal that is corrupted by the interference. It is shown that the signal coherence in the slow-time domain can be utilized to perform signal reconstruction in the slow-time. Moreover, it is shown that by compressing the interference in the time domain using pulse compression, the duration of the interference can be shortened, and an improvement in signal reconstruction performance can be achieved.

Given the complexity of the mutual interference problem, deep learning-based approaches can be instrumental in interference mitigation. This research also investigates the use of deep neural network architectures such as recurrent neural networks, convolutional neural networks, and convolutional autoencoders for signal reconstruction and denoising performance. 

Place, publisher, year, edition, pages
Karlshamn: Blekinge Tekniska Högskola, 2023
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 2
Keywords
automotive radar, autoregressive modeling, digital beamforming, interference mitigation, machine learning, mutual interference, pulse compression, radar signal processing, signal reconstruction
National Category
Engineering and Technology Signal Processing
Research subject
Systems Engineering
Identifiers
urn:nbn:se:bth-24272 (URN)978-91-7295-449-6 (ISBN)
Public defence
2023-03-30, Amazonas, Campus Karlshamn, Karlshamn, 09:00 (English)
Opponent
Supervisors
Available from: 2023-02-08 Created: 2023-02-08 Last updated: 2023-03-01Bibliographically approved

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Rameez, MuhammadPettersson, MatsDahl, Mattias

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