Kalman Filter Implementation for GPS Data Reading from TXT Files

Kalman filtering is a widely-used algorithm for processing noisy sensor data, particularly well-suited for GPS positioning data. For beginners, implementing a program that reads GPS data from TXT files and applies Kalman filtering serves as an excellent introductory project for learning data filtering and sensor fusion techniques.

The program workflow can be divided into three main components:

First is the file reading module. We need to read GPS data line by line from a TXT file, which typically includes timestamps, latitude, longitude, and possibly additional information such as velocity and altitude. When processing text data, special attention must be paid to data format parsing and exception handling, such as skipping empty lines or incorrectly formatted data. In code implementation, this would involve using file I/O functions like fopen() and fscanf() in MATLAB, or Python's open() and readline() methods, along with string parsing functions to extract numerical values.

Second is the Kalman filter implementation. For GPS data, we typically establish a state vector to track position and velocity. During the initialization phase, we need to set the process noise and measurement noise covariance matrices - these parameters directly affect the filtering performance. For GPS data, measurement noise can be obtained from device specifications, while process noise requires adjustment based on the application scenario. The implementation involves defining state transition matrices and measurement matrices, with key functions including predict() for state prediction and update() for measurement correction.

Finally, the data processing pipeline. After reading each line of data, we first perform the prediction step, estimating the current position based on the previous state; then execute the update step, fusing the GPS measurements with predictions. The filtered results can be output to a new file or displayed in real-time. In code, this would typically involve a loop structure that processes each data point sequentially, with optional visualization using plotting functions.

Beginners can start with simplified models, such as initially processing only one-dimensional position data to understand basic principles before expanding to two-dimensional or three-dimensional spaces. Practical applications also need to consider factors like Earth's curvature and coordinate system transformations. It's important to note that Kalman filter performance highly depends on parameter settings, requiring experimental tuning to find optimal values through techniques like parameter sweeping and performance evaluation metrics.