关键词: 模糊聚类, 自适应, 多传感器, 隶属度影响因子, 数据融合

Abstract: For data fusion algorithm based on improved fuzzy clustering, there are some disadvantages such as inaccurate fusion and low reliability of fusion. In order to solve the data fusion problem of multiple homogenous sensors measuring a certain feature of the same target without prior knowledge, this paper presents a data fusion algorithm based on adaptive fuzzy C-means clustering, which mainly applies adaptive fuzzy C-means clustering to data fusion. The algorithm firstly introduces adaptive coefficients to find cluster subsets of different shapes and sizes in improved fuzzy clustering, making the fusion result more accurate. Secondly, Kalman filtering principle and neural network prediction method based on multilayer perceptron are applied to the error covariance estimation, which improves the credibility of the fusion. The experimental results show that compared with the four classical data fusion algorithms, the algorithm has better results in the fusion of the four simulated data sets with the real data sets, and the advantages are particularly obvious in criterion functions and fusion errors.

Key words: fuzzy clustering, adaptive, multi-sensor, membership degree influence factor, data fusion