Large data mixed attribute feature detection method based on Kalman algorithm

• Main Authors: Chen, Weisong, Xu, Jingyun, Zheng, Kaixian, Lin, Shengkai, Wu, Yican, Gan, Liju
• Format: Conference Proceeding
• Language: English
• Published: SPIE 29.11.2023
• ISBN: 1510671803; 9781510671805
• ISSN: 0277-786X
• DOI: 10.1117/12.3013347

Most of the current big data mixed attribute feature detection structures are set to be one-way, and the detection coverage is greatly limited, leading to a decline in the precision of big data feature detection. Therefore, the design, verification and analysis of big data mixed attribute feature detection method based on Kalman algorithm are proposed. According to the current feature detection, the basic feature decomposition is carried out first, and the multi-level method is adopted to break the limit of detection coverage, and the multi-level data clustering and index optimization detection structure is designed. Based on this, the Kalman measurement big data mixed attribute feature detection model is constructed, and the dual redundant data feature fuzzy processing is used to achieve feature detection. The final test results show that: after the comparison of the selected five test areas, combined with the Kalman algorithm, the precision rate of big data feature detection can reach more than 85%, indicating that this feature detection scheme has better practical application effect, stronger pertinence, more extensive detection coverage, and has practical application value.