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Signal Processing Approaches for Interference Mitigation in Automotive Radar Systems
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0002-7464-5704
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 [en]
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: urn:nbn:se:bth-24272ISBN: 978-91-7295-449-6 (print)OAI: oai:DiVA.org:bth-24272DiVA, id: diva2:1735160
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
List of papers
1. Analytical and Experimental Investigations on Mitigation of Interference in a DBF MIMO Radar
Open this publication in new window or tab >>Analytical and Experimental Investigations on Mitigation of Interference in a DBF MIMO Radar
2017 (English)In: IEEE transactions on microwave theory and techniques, ISSN 0018-9480, E-ISSN 1557-9670, Vol. 65, no 5, p. 1727-1734Article in journal (Refereed) Published
Abstract [en]

As driver assistance systems and autonomous driving are on the rise, radar sensors become a common device for automobiles. The high sensor density leads to the occurrence of interference, which decreases the detection capabilities. Here, digital beamforming (DBF) is applied to mitigate such interference. A DBF system requires a calibration of the different receiving channels. It is shown how this calibration completely changes the DBF beam pattern required to cancel interferences, if the system has no IQ receiver. Afterward, the application of DBF on a multiple-input multiple-output (MIMO) radar is investigated. It is shown that only the real aperture and not the virtual one can be used for interference suppression, leading to wide notches in the pattern. However, for any target the large virtual aperture can be exploited, even if interferers are blinded out. Moreover, the wide notches for interference suppression of the real aperture appear narrow in the virtual aperture for target localization. The results are verified by measurements with time-multiplexing MIMO radar.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2017
Keywords
Automotive radar, beamforming, multiple-input multiple-output (MIMO), radar receivers, radar systems, signal processing
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-14477 (URN)10.1109/TMTT.2017.2668404 (DOI)000401086300009 ()
Available from: 2017-06-13 Created: 2017-06-13 Last updated: 2023-02-08Bibliographically approved
2. Adaptive digital beamforming for interference suppression in automotive FMCW radars
Open this publication in new window or tab >>Adaptive digital beamforming for interference suppression in automotive FMCW radars
2018 (English)In: 2018 IEEE Radar Conference, (RadarConf 2018), Institute of Electrical and Electronics Engineers Inc. , 2018, p. 252-256Conference paper, Published paper (Refereed)
Abstract [en]

This paper addresses the problem of mutual interference between automotive radars. This problem is getting more attention with an increase in the number of radar systems used in traffic. An adaptive digital beamforming technique is presented here which suppresses the interference without the exact knowledge of the interfering signal's Direction of Arrival (DoA). The proposed technique is robust and does not rely on any calibration for the interference cancellation. The adaptive interference suppression method is evaluated using a simulated scenario. Up to about 20-23 dB improvement in the target Signal to Interference and Noise Ratio (SINR) is measured in the simulation and a better detection performance is achieved using the proposed interference suppression technique. © 2018 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2018
Series
IEEE Radar Conference
Keywords
Beamforming, Frequency modulation, Radar systems, Signal to noise ratio, Adaptive digital beamforming, Adaptive interference suppression, Automotive radar, Detection performance, Interference cancellation, Interfering signals, Mutual interference, Target signals, Radar interference
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-16910 (URN)10.1109/RADAR.2018.8378566 (DOI)000442172700046 ()2-s2.0-85049977586 (Scopus ID)978-1-5386-4167-5 (ISBN)
Conference
2018 IEEE Radar Conference,Oklahoma City
Available from: 2018-08-20 Created: 2018-08-20 Last updated: 2023-02-08Bibliographically approved
3. Experimental Evaluation of Adaptive Beamforming for Automotive Radar Interference Suppression
Open this publication in new window or tab >>Experimental Evaluation of Adaptive Beamforming for Automotive Radar Interference Suppression
2020 (English)In: IEEE Radio and Wireless Symposium, IEEE, 2020, p. 183-186, article id 9049982Conference paper, Published paper (Refereed)
Abstract [en]

Mutual interference between automotive radars can make it difficult to detect targets, especially the weaker ones, such as cyclists and pedestrians. In this paper, the interference suppression performance of a Least Mean Squares (LMS) algorithm-based adaptive beamformer is evaluated using measurements from a 77 GHz Frequency Modulated Continuous Wave (FMCW) radar in an outdoor environment. It is shown that the adaptive beamformer increases detection performance and that the interference is suppressed down to the noise floor of the radar in the Range-Doppler domain. In the paper, real baseband sampling and complex-baseband sampling (IQ) radar receivers are compared in the context of interference suppression. The measurements show that IQ receivers are more beneficial in the presence of interference.

Place, publisher, year, edition, pages
IEEE, 2020
Series
IEEE Radio and Wireless Symposium, ISSN 2164-2958
Keywords
Automotive radar, Frequency Modulated Continuous Wave (FMCW), interference mitigation, digital beamforming
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-19368 (URN)10.1109/RWS45077.2020.9049982 (DOI)000565685500049 ()978-1-7281-1120-9 (ISBN)
Conference
IEEE Radio and Wireless Symposium, RWW 2020; San Antonio; United States; 26 January 2020 through 29 January 2020
Note

Sponsorer: AESS,APS,IEEE,MTT-S

Available from: 2020-04-07 Created: 2020-04-07 Last updated: 2023-02-08Bibliographically approved
4. Autoregressive Model-Based Signal Reconstruction for Automotive Radar Interference Mitigation
Open this publication in new window or tab >>Autoregressive Model-Based Signal Reconstruction for Automotive Radar Interference Mitigation
2021 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, no 5, p. 6575-6586Article in journal (Refereed) Published
Abstract [en]

Automotive radars have become an important part of sensing systems in vehicles and other traffic applications due to their accuracy, compact design, and robustness under severe light and weather conditions. The increased use of radars in various traffic applications has given rise to the problem of mutual interference, which needs to be mitigated. In this paper, we investigate interference mitigation in chirp sequence (CS) automotive radars via signal reconstruction based on autoregressive (AR) models in fast-and slow-time. The interference is mitigated by replacing the disturbed baseband signal samples with samples predicted using the estimated AR models. Measurements from 77 GHz frequency modulated continuous wave (FMCW) static and moving radars are used to evaluate the signal reconstruction performance in terms of the signal-to-interference-plus-noise ratio (SINR), peak side-lobe level (PSLL), and mean squared error (MSE). The results show that the interference is suppressed down to the general noise floor, leading to an improvement in the SINR. Additionally, enhanced side-lobe suppression is achieved via AR signal reconstruction, which is compared to a commonly used inverse-cosine method. Furthermore, the paper notes that the slow-time signal reconstruction can be more beneficial for interference suppression in certain scenarios. CCBY

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
Automotive radar, Autoregressive (AR) modelling, Chirp Sequence (CS), Frequency Modulated Continuous Wave (FMCW), interference mitigation, signal reconstruction
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing
Identifiers
urn:nbn:se:bth-19375 (URN)10.1109/JSEN.2020.3042061 (DOI)000616329300107 ()2-s2.0-85097401154 (Scopus ID)
Note

open access

Available from: 2020-04-07 Created: 2020-04-07 Last updated: 2023-02-08Bibliographically approved
5. Interference Compression and Mitigation for Automotive FMCW Radar Systems
Open this publication in new window or tab >>Interference Compression and Mitigation for Automotive FMCW Radar Systems
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
Keywords
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:nbn:se:bth-23838 (URN)10.1109/JSEN.2022.3204505 (DOI)000870341900066 ()
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
6. Signal Reconstruction Using Bi-LSTM for Automotive Radar Interference Mitigation
Open this publication in new window or tab >>Signal Reconstruction Using Bi-LSTM for Automotive Radar Interference Mitigation
2021 (English)In: 2021 18th European Radar Conference, EuRAD 2021, Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 74-77Conference paper, Published paper (Refereed)
Abstract [en]

Automotive radar has emerged as an important sensor for environmental perception in modern vehicles. A rapid increase in the number of radars present in traffic operating at unregulated frequencies has given rise to a mutual interference problem. In order for radar-based systems to function reliably, such interference must be mitigated. In this paper, this problem is addressed with a bidirectional long short-term memory (Bi-LSTM) network as a deep learning approach. Using the Bi-LSTM network, we reconstruct the intermediate frequency (IF) signal and recover samples lost to interference. The proposed signal reconstruction method is evaluated via real measurement data. The proposed Bi-LSTM network provides a better performance than an autoregressive model-based signal reconstruction method. © 2022 European Microwave Association (EuMA).

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2021
Keywords
FMCW radar, Interference suppression, Radar signal processing, Recurrent neural networks, Signal reconstruction, Automotive radar, Continuous wave radar, Frequency modulation, Long short-term memory, Radar equipment, Radar interference, Environmental perceptions, Interference mitigation, Interference problems, Learning approach, Memory network, Mutual interference, Reconstruction method, Signals reconstruction
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-23510 (URN)10.23919/EuRAD50154.2022.9784516 (DOI)000838709300018 ()2-s2.0-85133132279 (Scopus ID)9782874870651 (ISBN)
Conference
18th European Radar Conference, EuRAD 2021, London, 5 April 2022 through 7 April 2022
Available from: 2022-08-11 Created: 2022-08-11 Last updated: 2023-02-08Bibliographically approved
7. Performance Analysis of Deep Learning Algorithms for Automotive Radar Interference Mitigation
Open this publication in new window or tab >>Performance Analysis of Deep Learning Algorithms for Automotive Radar Interference Mitigation
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Modern vehicles are often equipped with a number of radar sensors that allow the vehicle to be aware of its surroundings for safety and automated driving features. As the number of radars in traffic is growing, mutual interference between radars on different vehicles is becoming unavoidable. Several deep learning methods for mitigating automotive radar interference have been proposed, with the primary differences being data type and network architecture. In this paper, we examine various deep learning-based interference mitigation strategies and investigate the benefits and drawbacks of various model versions. The selected deep learning model variants (convolutional neural networks (CNN) and autoencoders (AE) in time and range-Doppler domain) are trained on a combination of real and simulated radar data with simulated interference. The trained models are then evaluated on a different set of real measurements data with interference from two real sources. The improvement in Signal to Interference and Noise (SINR) in range-Doppler domain methods (~15 dB) over time domain methods (~5 dB) demonstrates excellent denoising capabilities of the range-Doppler domain methods. On the other hand, time-domain approaches are effective at reducing phase errors.

National Category
Engineering and Technology Signal Processing
Research subject
Systems Engineering
Identifiers
urn:nbn:se:bth-24271 (URN)
Available from: 2023-02-08 Created: 2023-02-08 Last updated: 2023-02-08Bibliographically approved

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