Multi-Feature Fusion Recognition Method Based on Information Entropy and Classification Decision Tree

Aiming at the characteristics of radar network measurement data, such as incomplete information and large uncertainty, a feature layer fusion recognition algorithm based on information entropy decision tree classification is proposed. The algorithm does not need training samples, adopts the way of construction-side classification, selects the attribute with the least information entropy as the classification attribute to classify the measured data, and realizes the recognition of the target. The simulation results show that the algorithm can make full use of the feature information of the measured data, and is a simple and effective feature layer fusion recognition algorithm.