Trajectory-Regularized Localization in Asynchronous Acoustic Networks via Enhanced PSO Optimization

Indoor localization of fast-moving targets under asynchronous acoustic sensing is severely constrained by non-line-of-sight (NLOS) propagation and sparse anchor deployments. To overcome these limitations, we propose a trajectory reconstruction-based framework that simultaneously exploits time-of-arrival (ToA) and frequency-of-arrival (FoA) measurements. By embedding temporal continuity and motion dynamics into the localization model, we cast the problem as a constrained nonlinear least squares optimization over the entire trajectory rather than isolated snapshots. To efficiently solve this high-dimensional problem, we design an enhanced particle swarm optimization (PSO) algorithm featuring adaptive phase switching and noise-resilient updates. Simulation results under varying noise conditions show that our method achieves superior accuracy and robustness compared to conventional least squares estimators, especially for high-speed trajectories. Real-world experiments using a passive acoustic testbed further validate the effectiveness of the proposed framework, with over 90% of localization errors confined within 3 m. The method is model-driven, training-free, and scalable to asynchronous and anchor-sparse environments.