New Data Fusion Algorithm Based on Fuzzy Closeness Degree

This article presents a comprehensive discussion on implementing a new data fusion algorithm based on fuzzy closeness degree. We begin by introducing the fundamental background and underlying principles of the algorithm, followed by providing complete MATLAB source code that enables readers to easily implement and experiment with the method. The implementation approach includes key functions for calculating fuzzy similarity measures, data normalization procedures, and fusion weight determination based on closeness degree calculations. The algorithmic workflow involves constructing membership functions, computing closeness degrees between data sources, and applying weighted fusion operations using vectorized MATLAB operations for optimal performance. This work aims to assist researchers interested in data fusion and fuzzy closeness degree algorithms by providing both theoretical insights and practical implementation tools. We include detailed explanations of core MATLAB functions such as data preprocessing routines, fuzzy set operations, and fusion optimization techniques. Additionally, we analyze the algorithm's advantages and limitations, along with potential application domains in signal processing, multi-sensor systems, and decision support scenarios. The discussion helps readers better understand the algorithm's practical performance characteristics and suitability for various real-world applications, supported by computational efficiency considerations and parameter tuning guidelines within the MATLAB environment.