In the decision-making system for target recognition, the ambiguity and uncertainty of multi-detector and multi-source information fusion and the conflict and mutual exclusion of the information obtained in each detection cycle will cause inaccurate recognition of the target. To solve this problem, a decision-making method for target recognition based on the cloud model and improved D-S (Dempster-Shafer) evidence theory is proposed. First, the linguistic evaluation value of target recognition accuracy is divided into different evaluation interval levels, and the information obtained from each detection period of each detector is converted into a cloud decision matrix based on the standard cloud of different evaluation levels to obtain the membership degree of each level of each period, and then a basic probability distribution function (mass function) is constructed. Second, based on the evidence theory, three types of parameters, namely conflict degree, difference degree, and dispersion degree, are introduced to measure the magnitude of conflict. A new evidence conflict parameter is defined, and the evidence conflict fusion algorithm is improved to revise and combine the evidence body of each detector. Thirdly, the mass function of the comprehensive decision making for each target recognition is obtained by combining the weight of each detector to make a decision on the target. Finally, a numerical example is used to verify the applicability of this method, and a comparison with other methods demonstrates the superiority of this method.
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