Design of UAV Flight State Recognition System for Multisensor Data Fusion

With the impact of artificial intelligence on the traditional unmanned aerial vehicle (UAV) industry, autonomous UAV flight has become a popular research field at present. Based on the demand for research on key technologies for autonomous UAV flight, this article studies UAV flight state recognition. This article is based on multisensor acquisition of UAV on-board information and uses the collected information for data fusion to complete UAV flight state identification. First, UAV flight data acquisition and data preprocessing are carried out; second, UAV flight trajectory features are extracted based on multidimensional data fusion; finally, UAV flight state recognition model based on principal component analysis-directed acyclic graph support vector machine (PCA-DAGSVM) model is established. The results show that the algorithm based on multisensor data fusion has good recognition effect in the UAV flight state recognition problem. The recognition accuracy of the algorithm exceeds that of the random forest model, and its accuracy in the training set of UAV flight state recognition is more than 90%, and its accuracy in the test set is more than 80%.