Kalman Filter Design with MATLAB Implementation

This article presents the design of a Kalman filter using MATLAB, a widely adopted filtering technique in signal processing applications. The Kalman filter primarily performs system state estimation and prediction by analyzing observational data and system models. Through its recursive algorithm combining prediction and correction steps, it accurately estimates system states while effectively eliminating random errors. The Kalman filter operates as an adaptive filter that dynamically adjusts its parameters based on real-time data inputs, making it suitable for varying environments and signal conditions. In MATLAB implementation, key functions like "kalman" or custom implementations typically involve state-space modeling, noise covariance matrices (Q and R), and recursive update equations. This article will detail the fundamental principles of Kalman filtering, the design workflow including initialization, prediction, and update phases, along with practical application scenarios. The content aims to provide valuable references and guidance for implementing robust Kalman filters in engineering applications.