A Decoupled Localization and Synchronization Method for Moving Targets Using Sequential One-Way TOA Measurements

With the rapid advancement of the Internet of Things (IoT), localization and synchronization (LAS) have become essential for moving targets in various applications. Existing decoupled LAS methods often suffer from significant performance degradation, while joint LAS approaches typically require motion modeling, introducing intermediate variables that compromise accuracy. In this paper, we propose a decoupled LAS method for moving targets based on sequential time-of-arrival (TOA) measurements. Building on our previous work, P-TDOA, which addresses instantaneous time-difference-of-arrival (TDOA) estimation in time-division broadcast positioning systems using a polynomial model of TOA and TDOA measurements, we formulate the LAS problem within a two-step framework. Specifically, we introduce two decoupled LAS (DLAS) approaches: one based on least squares and the other on weighted least squares. To evaluate the proposed methods, we derive the Cramér-Rao Lower Bounds (CRLBs) for LAS across multiple frames, considering scenarios both with and without the polynomial model. Numerical experiments validate the effectiveness of our method and its superiority over state-of-the-art joint LAS methods, highlighting its strong potential for applications in moving agent swarms.