Multi-Sensor Data Fusion Based on Extended Kalman Filter (EKF)

In cluttered environments where multiple interference sources may exist, multi-sensor data fusion technology serves as a critical approach for enhancing system robustness and accuracy. The Extended Kalman Filter (EKF)-based data fusion algorithm is widely adopted for multi-sensor integration, involving preprocessing of individual sensor measurements followed by fusion through the EKF algorithm to produce more reliable and precise data outcomes. Key implementation aspects include: - Sensor data preprocessing (noise filtering and outlier detection) - Linearization of nonlinear system models using Jacobian matrices - Covariance propagation for uncertainty management - Sequential measurement updates from multiple sensors The algorithm typically implements these steps: 1. Initialize state vector and error covariance matrix 2. Predict state and covariance using system dynamics 3. Update predictions with sensor measurements via Kalman gain 4. Fuse data from multiple sensors using weighted averaging Therefore, EKF-based multi-sensor data fusion demonstrates significant research value and application potential in clutter-affected environments, particularly for target tracking, navigation systems, and autonomous vehicle perception where accurate state estimation is crucial under challenging conditions.