Combat Intention Recognition of Air Targets Based on 1DCNN-BiLSTM

In air combat, target intent recognition is the premise and foundation of battlefield situation awareness and intelligent decision-making. Aiming at the problem that traditional intention recognition methods cannot deal with a large amount of continuous target data, an air target combat intention recognition model based on one-dimensional convolutional neural networks and bidirectional long short-term memory (1DCNN-BiLSTM) is proposed. First, the target data is divided into fixed-size continuous subsequences by time sliding window on the basis of determining the target feature space and intention space. Second, the convolution operation is performed on the target sequence through the 1DCNN module as a means of extracting the features of the target attributes in the time dimension, and at the same time reducing the dimensionality of the target sequence, so as to facilitate the subsequent processing of the target data. Then, the BiLSTM module is utilized to capture the dependencies on the longer distance of the target sequence from both forward and reverse directions simultaneously. Finally, the optimal model structure and hyperparameters of 1DCNN-BiLSTM are not only determined through experiments, but also the validity of each part of the model is verified. Compared with the traditional methods, the model proposed in this paper effectively improves the accuracy of combat intention recognition of air targets, and provides the essential basis and auxiliary support for the decision-making of the commanders.