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

Area of Science:

• Acoustic signal processing
• Indoor localization systems
• Motion tracking

Background:

• Indoor localization of fast-moving targets is difficult due to non-line-of-sight (NLOS) propagation and limited sensor (anchor) placement.
• Existing methods struggle with asynchronous data and sparse deployments.

Purpose of the Study:

• To develop a robust trajectory reconstruction framework for indoor acoustic localization.
• To overcome limitations of NLOS propagation and sparse anchor deployments for fast-moving targets.

Main Methods:

• A novel framework integrating time-of-arrival (ToA) and frequency-of-arrival (FoA) measurements.
• Constrained nonlinear least squares optimization over the entire trajectory, incorporating temporal continuity and motion dynamics.
• An enhanced particle swarm optimization (PSO) algorithm with adaptive phase switching and noise-resilient updates.

Main Results:

• Superior accuracy and robustness compared to conventional least squares estimators, particularly for high-speed trajectories.
• Real-world experiments demonstrated over 90% localization errors within 3 meters.
• The method proved effective in asynchronous and anchor-sparse environments.

Conclusions:

• The proposed trajectory reconstruction framework significantly enhances indoor acoustic localization for fast-moving targets.
• The model-driven, training-free approach offers a scalable and effective solution for challenging environments.
• The integration of ToA/FoA and advanced PSO provides a robust and accurate localization system.