Information Fusion Based on Kalman Filter

Kalman filter is a classic recursive algorithm widely used in state estimation and information fusion for dynamic systems. By combining predictive models with actual observation data, it effectively reduces noise interference and provides optimal state estimation results. Particularly in multi-sensor systems, Kalman filter can fuse data from different sources to enhance overall system accuracy and reliability.

Implementing Kalman filter-based information fusion in MATLAB typically involves several key steps: First, establish the system's state-space model, including state equations and observation equations, to define the system dynamics and sensor measurement methods. Second, initialize Kalman filter parameters such as initial state estimates, process noise covariance matrix, and observation noise covariance matrix. In code implementation, this involves setting matrices like Q (process noise) and R (measurement noise). Then, during each iteration, execute two phases: prediction and update. The prediction phase uses the system model to estimate the current state and its uncertainty through equations like x_pred = A*x_prev, while the update phase incorporates new observation data to correct predictions using the Kalman gain calculation K = P_pred*H'/(H*P_pred*H' + R).

In practical applications, Kalman filters are commonly used in navigation systems, target tracking, and robot localization. For example, in UAV positioning, data from GPS, IMU (Inertial Measurement Unit), and visual sensors can be fused using Kalman filter to eliminate single-sensor errors and obtain more stable position and attitude estimates. MATLAB provides comprehensive tool functions such as `kalman` for linear systems or `extendedKalmanFilter` for nonlinear cases, simplifying implementation where developers can focus on model design and parameter tuning through functions like `predict` and `correct`.

The core of information fusion lies in rationally utilizing the advantages of different sensors, and the mathematical framework of Kalman filter provides a solid theoretical foundation for this. By appropriately adjusting noise covariance and model parameters through iterative tuning in code, the system can adapt to various application requirements, achieving efficient and robust multi-source data fusion.