This paper presents an approach based on Doppler beam sharpening (DBS) to enhance the resolution of multiple ‘dynamic’ targets in automotive driving scenarios. The ambiguity inherent to the forward-looking DBS and the coupling between azimuth and elevation angles are jointly addressed with the proposed method. Demonstrated experimental results show the feasibility of the proposed technique in automotive radar sensing.

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Autonomous driving is one of the most popular research topics. Radar technology is used for many applications of ADAS and is considered one of the key technologies for HAD. It has unique advantages compared with other sensors, especially its capabilities during adverse weather conditions and Doppler information extraction. Required by autonomous applications, radar has to change its historical role from a simple detector to an imaging sensor, which requires not only the range and Doppler resolution ability but also a high spatial resolution, i.e., the azimuth and the elevation angle resolution. To address this problem, in this thesis, new signal processing algorithms are proposed, which pave the way to improved performance of the automotive radar sensor.
The FMCW waveform is widely used in current automotive applications due to its low cost and simplicity. MIMO array techniques exploit the spatial diversity of transmit and receive antenna arrays and have been exploited in current automotive radar because of their ability to achieve high angular resolution with a few antennas. Platform movement is one of themain characteristics of automotive radar, which introduces movement uncertainty compared with radars at fixed locations but provides an opportunity to use the movement to boost the angular resolution. Thus, FMCW waveform and MIMO antenna array are the main research subjects in this thesis.....@en

The problem of 3D ego-motion velocity estimation using multichannel Frequency Modulated Continuous Wave (FM CW) radar sensors has been studied. Special attention is given to presence of moving targets in the scene. These targets are first distinguished by the difference between the measured Doppler, and the Doppler calculated with an initial rough estimation of the vehicle ego-velocity. Then, an iterative algorithm is proposed to reduce the influence of the moving targets in the ego-motion estimation procedure, thus improving the overall accuracy. The performance of the proposed algorithm is compared with state-of-the-art alternatives based on simulated data, and superior performance has been demonstrated.

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The problem of estimating the 3D ego-motion velocity using multi-channel FMCW radar sensors has been studied. For the first time, the problem of ego-motion estimation is treated using radar raw signals. A robust algorithm using multi-channel FMCW radar sensors to instantly determine the complete 3D motion state of the ego-vehicle (i.e., translational speed and rotational speed) is proposed. The angle information of targets is extracted, and then their phase information from different times instances is used to determine vehicle ego-motion through an optimization process. Any pre-processing steps, such as clustering or clutter suppression, are not required. The performance of the algorithm is compared with the state-of-the-art algorithms based on real-world data, and superior performance has been demonstrated. The algorithm proposed can be easily integrated into radar signal processing pipelines for other tasks relevant to autonomous driving.@en

The ambiguity problem in forward-looking Doppler beam sharpening is considered. Doppler beam sharpening (DBS) has shown its potential to improve cross-range resolution for automotive radar applications. However, it suffers from ambiguities when targets are positioned symmetrically with respect to the vehicle trajectory. A new approach named 'Robust Unambiguous DBS with Adaptive Threshold' (RUDAT) is proposed to address the problem of ambiguities. It combines DBS with multiple-input-multiple-output (MIMO) radar processing, and is robust to non-ideal movements of the vehicle and fluctuations in the targets' reflectivity. The performance of the proposed method is compared to existing approaches using simulated data with point-like and extended targets, demonstrating good preliminary results.@en

The problem of high-resolution direction-of-arrival (DOA) estimation based on a limited amount of snapshots in automotive multiple-input multiple-output (MIMO) radar has been studied. The number of snapshots is restricted to minimize target spread/migration in range and/or Doppler domains. A computationally efficient approach for side-looking arrays is developed, which combines the generation of motion-enhanced snapshots and MIMO technology, thus exploiting the movement of the vehicle and the spatial diversity of the transmit and receive antennas. Due to motion, a larger virtual aperture is obtained and the angular resolution is boosted, achieving the separation of targets that the traditional MIMO approach cannot discriminate, as well as better results than with other single snapshot DOA estimation techniques. Algorithm performance has been studied in simulations, and possible limitations have been discussed. In addition, the method has been verified experimentally with pointlike and extended targets, and good agreement between simulations and experimental results has been observed.

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The ambiguity problem of targets in Doppler beam sharpening (DBS) with forward-looking radar is considered. While DBS is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the platform movement. To address this problem, an approach named unambiguous Doppler-based forward-looking multiple-input multiple-output (MIMO) radar beam sharpening scan (UDFMBSC) is proposed, based on the combination of MIMO processing and DBS. The performance of the proposed method is compared to existing approaches using simulated data with point-like and extended targets. The method is successfully verified using experimental data.

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A method exploiting the movement of the vehicle to boost the cross-range resolution of automotive radar by forming a larger virtual array is proposed. Initial simulated results show that the proposed method with the traditional Digital beamforming (DBF) algorithm can separate targets that cannot be otherwise recognized by the traditional MIMO approach. Furthermore, the proposed approach does not require prior knowledge of the number of targets, and can solve the MUSIC rank deficiency problem because of its larger virtual planar antenna.@en

A direction-finding approach for arrays with a limited number of antenna elements has been investigated. A method based on the harmonic analysis of the received signal has been proposed to solve it. The angle estimation accuracy has been improved by angle searching and peak detection. The proposed method is theoretically described and numerical simulations are provided to verify its effectiveness. Compared with classical direction-finding methods with limited antenna elements, significant improvements have been demonstrated.

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