Distributed MIMO Radar Network for IoT: High-Resolution 4-D Point Cloud Generation and Signal Processing for Smart Mobility

Abstract

The rapid advancement of Internet of Things (IoT) technology, particularly in the realm of autonomous driving, has elevated the requirements for automotive radar systems to achieve precise environmental perception. This article delves into the distributed MIMO radar network model and the associated signal processing techniques that enable accurate measurement of range, velocity, and angular positions, culminating in the generation of high-resolution 4-D point clouds. These capabilities are pivotal for intelligent interactions between vehicles and their surroundings within the IoT ecosystem. We introduce a stepped-frequency frequency-modulated continuous wave (SF-FMCW) waveform that incrementally increases the starting frequency of each chirp, leading to a larger bandwidth and finer range resolution without altering the individual chirp bandwidth. Furthermore, we propose a hybrid FDM-DDM scheme to ensure orthogonality among MIMO waveforms. This scheme allows for decoding across various DDM modes through the application of overlapping binary masks, while maintaining unambiguous range-Doppler measurements, which is crucial for real-time data processing and decision-making within IoT. To enhance angular resolution, we optimize the array configuration and develop a low sidelobe direction of arrival (DOA) estimation method using phase coherence factor (PCF) techniques. Extensive simulations and experimental analyses demonstrate the superior performance of the proposed methods in resolving closely spaced targets and generating high-fidelity 4-D point clouds, even in challenging scenarios with limited angular separation. The development of these technologies is significant for intelligent perception and safe navigation of vehicles within the IoT, providing a technological foundation for seamless integration of vehicles with the IoT infrastructure.