Abstract
With the rapid development of the Internet of Things (IoT), autonomous vehicles have been receiving more and more attention because they own many advantages compared with traditional vehicles. A robust and accurate vehicle localization system is critical to the safety and the efficiency of autonomous vehicles. The global positioning system (GPS) has been widely applied to the vehicle localization systems. However, the accuracy and the reliability of GPS have suffered in some scenarios. In this paper, we present a robust and accurate vehicle localization system consisting of a bistatic passive radar, in which the performance of localization is solely dependent on the accuracy of the proposed off-grid direction of arrival (DOA) estimation algorithm. Under the framework of sparse Bayesian learning (SBL), the source powers and the noise variance are estimated by a fast evidence maximization method, and the off-grid gap is effectively handled by an advanced grid refining strategy. Simulation results show that the proposed method exhibits better performance than the existing sparse signal representation-based algorithms, and performs well in the vehicle localization system.
Highlights
The emergence of the Internet of Things (IoT) and its applications have greatly affected the society and improved the quality of life [1,2]
The IoT has been widely used in the autonomous vehicles [3,4], which are viewed as a potential solution to some critical problems, such as pollution, traffic congestion, and road accidents [5]
We provide a vehicle localization system consisting of a passive bistatic radar and the vehicle localization method via direction of arrival (DOA) estimation
Summary
The emergence of the Internet of Things (IoT) and its applications have greatly affected the society and improved the quality of life [1,2]. The accuracy and availability of the GPS signal cannot always meet the requirements of autonomous vehicle localization, due to satellite visibility interruption and signal multipath To address this issue, many vehicle localization systems based on robust location algorithms with advanced sensors, such as RADAR [7], LiDAR [8,9], and camera [10], have been invented and become the focus of research. A robust SBL based DOA estimation approach is proposed to address the non-uniform noise as well as the off-grid issue. To our best knowledge, vehicle localization systems using passive bistatic radar with SBL-based DOA estimation have rarely been studied in the existing literature, despite their high usefulness in practice. It is worthwhile to investigate the vehicle localization systems using passive bistatic radar with an advanced SBL based DOA estimation algorithm. A vehicle localization simulation is conducted to verify the feasibility of the proposed vehicle localization method
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