Abstract: Aiming at the problem that the data obtained by different sensors might be uncertain and inconsistent due to the influence of  sensor’s own error and external interference, in order to effectively eliminate the contradictions and conflicts between data and improve the data fusion effect, we proposed a multi-ensor data fusion algorithm based on fuzzy clustering. Firstly, the D-S (Dempster-Shafer) evidence theory was used for preliminary data fusion, the distance between heterogeneous data was calculated and the corresponding trust function was determined, and different sensor data were corrected and coordinated to improve data consistency. Secondly, we introduced fuzzy clustering method to optimize the preliminary fusion results of multi-sensor data, 
grouped data points into clusters with similar features, and determined the initial clustering center. Finally, we used  fuzzy clustering algorithm to group data and improve the accuracy and robustness of data fusion results. The experimental results show that the proposed algorithm has high fitting goodness and shard reception rate in multi-sensor data fusion, and overall energy consumption is low, with excellent overall performance.

Key words: fuzzy clustering, multi-sensor, data fusion, D-S evidence theory