The book employs various mathematical tools and techniques to design and analyze Multi-Sensor Filtering Fusion (MSFF) algorithms. It utilizes the recursive Riccati equation to derive optimal filter gains for state estimation, ensuring stability and performance. Matrix decomposition techniques are applied to simplify complex matrices and facilitate the design process. Optimal estimation theory is used to determine the best possible estimates given the available information, enhancing the accuracy of the fusion algorithms. These methods are integrated to address challenges like censored data and communication constraints, resulting in robust and efficient MSFF algorithms suitable for complex systems.