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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.

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).

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 open access

Radar has emerged as an important sensor for scenario perception in automated driving and surveillance systems. The exponential increase of radar units in traffic and their operating frequency limitations have given rise to the problem of mutual interference. Radar's performance degrades in the presence of interference, which can result in false alarms and missed detections. In the case of safety-oriented systems (such as automatic emergency braking, blind-spot detection and obstacle detection at level crossings), radar's degraded performance can result in accidents. Therefore, it is important to mitigate the effect of mutual interference to make modern radar applications safe and reliable. The goal of this work is to develop signal processing techniques for interference mitigation in frequency modulated continuous wave (FMCW) radars operating at 77-81 GHz. The thesis investigates radar interference suppression in the spatial domain, using antenna arrays. The interference is suppressed by placing notches in the antenna radiation pattern in the direction of the interference source by employing digital beamforming. The array aperture (size) determines the beam-width and notch resolution of the receiving antenna. Narrow notches are desirable since they lead to a smaller suppressed region in the radar's field of view. It is demonstrated that an extended virtual aperture in a multiple-input-multiple-output (MIMO) FMCW radar does not offer an improved notch resolution for interference suppression due to a non-coherent interference signal in the virtual aperture. Moreover, it is shown that the calibration mismatches of the receiving array completely change the final antenna beam-pattern compared to the theoretical one. Additionally, an adaptive beamforming approach of interference suppression based on the least mean squares (LMS) algorithm is presented, which is evaluated using outdoor measurements from a 77GHz FMCW radar. The results demonstrate that the proposed technique suppresses interference successfully, resulting in a signal to interference plus noise ratio (SINR) improvement. It is also shown that complex-baseband (IQ) receivers achieve better interference suppression compared to real-baseband receivers when spatial domain methods are employed. The final research publication deals with interference mitigation in the time-domain intermediate frequency signal. The disturbed samples in the received signal are detected, removed, and reconstructed based on an estimated autoregressive (AR) signal model. The baseband signal coherence in both fast- and slow-time makes it possible to perform signal reconstruction in both dimensions. With the help of outdoor measurements covering selected scenarios, it is demonstrated that by carefully selecting the signal reconstruction dimension, a better SINR and side-lobe suppression can be achieved.

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. Afterwards, the application of DBF on a MIMO (multiple-input multiple-output) 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 a time-multiplexing MIMO radar., acceptedVersion