Enhancing Timeliness in Asynchronous Vehicle Localization: A Signal-Multiplexing Network Measuring Approach

Cooperation among entities within networks for information exchange and measurement is a promising paradigm for high-accuracy positioning in automated vehicles. However, due to imperfect clocks and inefficient wireless protocols, current cooperative positioning techniques have inadequate accuracy and timeliness. This paper presents a novel localization framework for connected automated vehicles (CAVs) capable of achieving high-accuracy relative positioning with high update rates. We design a signal-multiplexing network measuring (SNM) protocol to optimize the measurement update rates and propose new range estimations to achieve high-accuracy ranging against clock errors and mobility. Using range estimations, we develop a relative localization algorithm that leverages intra- and inter-node cooperation with coordinate reference alignment to reconstruct the geometric relationships among the nodes. Performance analyses and simulation results demonstrate that our method achieves high-accuracy positioning with timely updates, ensuring reliability and robustness in asynchronous vehicle localization.