MATLAB Code Implementation for GPS Single Point Positioning with Kalman Filtering

In this paper, we present the implementation of GPS single point positioning using MATLAB code combined with Kalman filtering method to achieve enhanced positioning accuracy. Our algorithm achieves a positioning precision of 20*40 meters, enabling more accurate location determination. The code implementation involves processing raw GPS measurements through Kalman filtering stages, including prediction and update steps. To validate our approach, we conducted comprehensive simulation analysis and provide result graphs below to help readers better understand our methodology and experimental outcomes.

By employing the Kalman filtering method, our implementation effectively reduces noise in GPS signals and improves positioning accuracy. The algorithm features state prediction and measurement update phases, where we model the system dynamics using state transition matrices and handle measurement uncertainties through covariance matrices. Additionally, our implementation includes position prediction capabilities for future time steps, which proves particularly valuable for applications requiring long-term target tracking. Our research demonstrates that compared to traditional single point positioning methods, our Kalman filter-based approach significantly enhances positioning accuracy and offers substantial advantages in practical applications.

We believe this research makes important contributions to GPS positioning technology development and provides valuable references for researchers in related fields. The MATLAB code structure includes modules for data preprocessing, Kalman filter initialization, recursive filtering operations, and result visualization, offering a comprehensive framework for GPS positioning applications.