MATLAB Simulation of Ultra-Wideband TOA Positioning Algorithm with Kalman Filter

In this article, we present a MATLAB simulation of the Ultra-Wideband (UWB) Time-of-Arrival (TOA) positioning algorithm enhanced with Kalman filtering. This high-precision localization technique is applicable in both indoor and outdoor environments, featuring low error rates and superior accuracy. UWB technology represents an emerging wireless communication standard characterized by high bandwidth, low power consumption, and exceptional precision. The implementation involves calculating TOA measurements from UWB signals to estimate distances between transmitters and receivers. The Kalman filter is then applied to refine these estimates by reducing noise and improving trajectory tracking. Key MATLAB functions include signal processing tools for TOA extraction and state-space modeling for Kalman filter implementation. We provide a detailed explanation of the algorithm's principles and implementation methodology, followed by MATLAB-based simulation analysis. The discussion covers performance metrics, advantages, and limitations of the approach, along with proposed enhancement strategies to further improve accuracy and practical applicability. The simulation includes code segments demonstrating distance calculation, noise modeling, and recursive filtering processes.