Multi-Sensor Data Fusion: Algorithm Implementation and MATLAB Simulation

Multi-sensor data fusion is a technique that enhances system performance by integrating information from multiple sensors. This technology finds extensive applications in autonomous driving, robotic navigation, and environmental monitoring. Through MATLAB simulations, we can effectively implement and test various data fusion algorithms. The core objective of data fusion is to reduce uncertainty and improve measurement accuracy. Common fusion algorithms include Kalman filtering, particle filtering, and Bayesian estimation. Kalman filtering is particularly suitable for linear Gaussian systems, continuously optimizing estimation results through prediction and update steps. In code implementation, Kalman filter typically involves defining state transition matrices, measurement matrices, and covariance matrices for noise handling. Implementing data fusion algorithms in MATLAB generally involves these steps: First, simulate multiple sensor data inputs that may contain noise using functions like randn() for Gaussian noise generation. Next, select appropriate fusion algorithms and code the corresponding logic - for instance, using MATLAB's kalman function or creating custom particle filter implementations with resampling procedures. Finally, compare pre-fusion and post-fusion data differences through simulation results to validate algorithm effectiveness using visualization tools like plot() and error analysis functions. When extending concepts, consider multi-source heterogeneous data fusion challenges, such as processing data from different sensor types (e.g., cameras, radar, LiDAR). This may involve coordinate transformation and time synchronization algorithms. Additionally, explore optimization techniques for high-real-time scenarios, including algorithm simplification, parallel computing using MATLAB's parfor, or embedded code generation with MATLAB Coder to reduce computational overhead.