Multi-Sensor Data Fusion with MATLAB Implementation

Multi-sensor data fusion is a technique that integrates data from various sensors to achieve more accurate and reliable results. This book demonstrates how to implement multi-sensor data fusion using MATLAB programming language, providing extensive simulation examples with detailed code explanations to help readers better understand the technology's applications and advantages. Through studying this book, readers will master the fundamental principles of multi-sensor data fusion and learn how to apply these techniques to solve real-world problems. The content includes practical MATLAB implementations covering key algorithms such as Kalman filtering, Bayesian estimation, and neural network-based fusion methods. Each chapter features executable code examples that illustrate sensor data preprocessing, fusion architecture design, and performance evaluation metrics. Additionally, the book explores applications of multi-sensor data fusion across various domains, including robotics (sensor integration for navigation and obstacle avoidance) and autonomous driving systems (LiDAR, camera, and radar data fusion). The MATLAB-based approach specifically demonstrates functions like sensor data alignment, uncertainty management, and fusion rule implementation using MATLAB's Sensor Fusion and Tracking Toolbox. This comprehensive guide will undoubtedly assist readers in gaining deeper insights and proficiency in multi-sensor data fusion technology through hands-on MATLAB programming examples and industry-relevant case studies.