Signal Estimation Using Kalman Filter Implementation with MATLAB

This article provides a comprehensive guide on implementing the Kalman filter algorithm for signal estimation, accompanied by complete MATLAB source code. The Kalman filter algorithm serves as a powerful estimation technique that processes noisy measurements to produce more accurate state estimates. We will delve into the fundamental principles of the Kalman filter, covering system modeling approaches, filter coefficient calculations, and practical application methods for signal value estimation. The implementation section demonstrates how to code the Kalman filter in MATLAB, featuring key functions like state prediction, measurement update, and covariance matrix management. Through detailed code examples, we show how to initialize filter parameters, implement the prediction-correction cycle, and handle real-time data processing. By studying this material, you will gain practical knowledge for applying Kalman filtering to solve real-world measurement problems and develop the skills to implement robust estimation systems using MATLAB's matrix operations and algorithmic capabilities.