MATLAB Implementation of Indoor Positioning Algorithms

This paper explores indoor positioning algorithms with specific implementations developed in the MATLAB environment. Our research provides detailed explanations of the algorithmic principles and implementation methodologies, incorporating code-level descriptions of key functions such as signal processing routines, triangulation calculations, and error correction mechanisms. We discuss the algorithm's advantages and limitations in indoor positioning applications, including MATLAB-specific performance considerations like matrix optimization techniques and real-time processing capabilities. Furthermore, we examine potential applications across broader domains and propose future research directions for enhancing and optimizing indoor positioning algorithms. The implementation includes MATLAB code segments demonstrating fingerprint matching techniques, Kalman filter integration for trajectory smoothing, and path loss modeling for signal strength calibration. This work aims to provide comprehensive technical insights into indoor positioning algorithms, enabling better understanding and practical application of the technology through well-documented MATLAB implementations.