Research on Extraction and Recognition of Military Aircraft Complex Flight Action

Abstract

Aircraft flight action recognition and its corresponding flight parameter data extraction are the key contents of flight training quality analysis. At present, the flight parameter data has features of big scale, high dimension and big redundancy. Therefore, this paper proposes an unsupervised aggregation dynamic time warping algorithm (UADTW) to reduce the complexity of DTW algorithm, help manual establish the sample data set quickly and extract the correlation characteristics of standard sequence. At the same time, according to the characteristics of complex flight action, a deep neural network model is constructed to learn the characteristics of flight action sequence, the difference characteristics and the correlation characteristics of standard sequence. Based on the deep neural network model, this paper designs a self selection feature layer and proposes a self-selective deep neural network(SDNN) model, which can independently select the features that contribute greatly to flight action recognition and improve the characterization of flight parameter data by feature representation. The practical application shows that the method of flight action extraction and recognition based on UADTW and SDNN can reduce the labor cost and effectively improve the accuracy of flight action recognition.

Keywords

• |aircraft flight action|self-selective mechanism|dynamic time warping|neural network|flight parameter data