Comparative Analysis of Various Algorithms in GPS Positioning Methods

This document provides a comprehensive examination of several positioning algorithms employed in Global Positioning System (GPS) technology, complemented by MATLAB simulation programs for enhanced understanding. GPS positioning represents a widely adopted localization technique that utilizes satellite signals to determine precise coordinates anywhere on Earth. Among GPS positioning methodologies, multiple algorithmic approaches are available, including least squares estimation, Kalman filtering, and particle filtering techniques. We conduct a systematic comparison of these algorithms' strengths and limitations, while investigating their practical applications across different scenarios. The MATLAB implementations demonstrate key computational aspects: least squares methods solve positioning equations through matrix operations like pinv() for pseudoinverse calculations; Kalman filters employ state-space models with predict-update cycles using functions like kalman(); particle filters utilize sequential Monte Carlo methods with resampling procedures. Each simulation includes parameter tuning examples and error analysis components. Through this technical exploration, readers will gain profound insights into GPS positioning methodologies and acquire practical knowledge for conducting simulation-based analysis using MATLAB's computational environment.