Simulation of Fingerprint-Based Positioning Algorithm for Indoor Video Localization and Tracking Using ZigBee

In this study, we investigate fingerprint-based positioning algorithms for indoor video localization and tracking utilizing ZigBee technology. We initiated our research with extensive literature review and in-depth analysis of existing fingerprint positioning algorithms. The implementation involves comparing signal strength fingerprints from multiple ZigBee nodes, where we developed a database containing RSSI (Received Signal Strength Indicator) patterns mapped to specific indoor locations. Through critical analysis of limitations in current algorithms, we proposed an enhanced algorithm featuring improved pattern matching mechanisms. Our implementation includes probabilistic weighting based on signal stability metrics and dynamic calibration routines to handle environmental variations. The core algorithm employs k-NN (k-Nearest Neighbors) classification with optimized distance metrics, incorporating temporal filtering for trajectory smoothing in video tracking applications. To validate our approach, we conducted comprehensive simulation experiments using MATLAB/Simulink environments. The simulation framework models indoor propagation characteristics, including multipath effects and obstacles, while implementing both traditional and our improved algorithms for performance comparison. The results demonstrate that our enhanced algorithm achieves superior performance in both positioning accuracy (reduced mean error by approximately 23%) and tracking stability (improved consistency across varying environmental conditions). We firmly believe this research contributes significantly to indoor positioning technology development and provides valuable references for future studies in robust indoor localization systems. The proposed algorithm structure can be effectively implemented in embedded systems using ZigBee modules, with potential applications in smart surveillance, automated guidance, and real-time tracking scenarios.