Angle Estimation and Target Detection with Automotive Radar

Abstract

This thesis focuses on advancing radar technology to meet the growing demands of autonomous driving, particularly regarding angular resolution and target detection. The work acknowledges the critical role of automotive radar in achieving higher levels of vehicle autonomy, where reliable detection, classification, and tracking of objects like pedestrians, vehicles, and infrastructure are critical. The primary focus of the dissertation is to explore novel techniques for enhancing radar system performance through machine learning (ML) and compressive sensing (CS) approaches, addressing the challenges faced by current radar technologies, such as low angular resolution and inefficient target detection in dynamic environments.

The dissertation begins by outlining the challenges in automotive radar systems, especially the need for improved angular resolution without increasing the physical size and complexity of the radar devices. The importance of angular resolution is emphasized for both azimuth and elevation, as modern vehicles must be able to discriminate between various objects, such as distinguishing between two vehicles at a similar distance, or identifying an object’s height to determine whether it can be driven under or must be avoided. Current methods for improving angular resolution, such as increasing the number of transmitters and receivers in multiple input multiple output (MIMO) radars, are costly and increase system complexity, thus requiring novel solutions to meet industry needs. Then, Chapter 2 briefly summarizes the theoretical background of MIMO radars and defines the terminology used in the rest of the dissertation. This is crucial since automotive radar is a multi-disciplinary topic with people from different backgrounds interacting, and often, the same concepts are named differently.

The first research chapter, Chapter 3, introduces a self-supervised learning framework designed to enhance the angular resolution of radar systems without the need for additional physical hardware. A neural network (NN) artificially expands the radar’s aperture by predicting the response of additional antenna elements based on data from radars with larger arrays. This approach leverages the correlation between antenna elements to generate a more detailed angular profile from a smaller, low-resolution radar, allowing for a more accurate estimation of incoming signals' direction of arrival (DoA). Extensive simulations and experimental results demonstrate that this method significantly enhances radar performance in separating closely spaced objects, which is critical in automotive scenarios.

Another key contribution of the dissertation is presented in Chapter 4, with the application of Bayesian compressive sensing (BCS) to automotive radar, which exploits the sparse nature of the data in the DoA domain. The BCS approach uses probabilistic models to estimate the DoA while also providing uncertainty measures, offering both accuracy and reliability in angular estimation. The research further explores how array topologies can be optimized for BCS-based DoA estimation, demonstrating that a carefully designed antenna array can achieve better performance with fewer elements, thus reducing system costs. Additionally, the work presents a computationally efficient BCS algorithm that dramatically reduces the time needed for DoA estimation without compromising accuracy. This is a crucial advancement for real-time applications, where fast processing is required for decision-making in autonomous driving.

In addition to BCS, in Chapter 5, total variation compressive sensing (TVCS) is applied to the problem of radar imaging. TVCS enforces sparsity in the gradient of the signal rather than in the signal itself, which proves particularly effective in estimating the shape of extended targets, such as vehicles or pedestrians. By applying TVCS to 2D and 3D radar data, the dissertation demonstrates that this method can reconstruct objects' shapes more accurately than traditional methods, thereby enhancing the radar's ability to classify and understand the surrounding environment. The application of TVCS marks a significant step forward in radar-based shape estimation, especially for imaging radars used in automotive systems.

The dissertation also addresses the limitations of conventional target detection methods, particularly the widely used window-based constant false alarm rate (CFAR) detectors. Window-based CFAR detectors struggle with dynamic and unpredictable environments, which are common in road traffic scenarios. Moreover, they are unsuitable for extended targets with very different sizes, such as the ones encountered in automotive radar. To overcome this, in Chapter 6, a deep learning-based detector is proposed, trained using a newly developed dataset, RaDelft, which includes synchronized radar and lidar data. This deep learning detector outperforms traditional CFAR detectors by significantly improving the probability of detection and the Chamfer distance, especially in complex and cluttered environments. The RaDelft dataset itself is another important contribution of the dissertation, providing the research community with a well-curated, large-scale, multi-sensor dataset for further exploration and development of radar-based detection and classification systems.

In conclusion, this dissertation presents a comprehensive study of methods to enhance the angular resolution, detection capabilities, and efficiency of automotive radar systems through a combination of machine learning and compressive sensing. It provides practical solutions verified with experimental data to overcome existing limitations in automotive radar technology, particularly in the areas of angular resolution, target detection, and data processing speed. These advancements contribute to the broader goal of achieving fully autonomous driving by improving the ability of radar systems to perceive and interpret complex environments.