Aiming at the problem of small UAVs threatening the low-altitude field and its difficulty in recognition under complex electromagnetic environment, a signal recognition algorithm based on convolutional neural network (CNN) is proposed.The characteristics of the direct sequence spread spectrum (DSSS) signal under the application of UAVs are analyzed,and the signal time-frequency characteristics are obtained by short-time Fourier transform (STFT).The energy threshold noise reduction method proposed is used to reduce the noise in the time-frequency features.The CNN is designed for classification and recognition.Simulation results show that the algorithm has strong robustness and low signal-to-noise ratio environmental adaptability,and its performance is significantly better than traditional algorithms.When the signal-to-noise ratio (SNR) is -6 dB,the recognition rate reaches 0.97.

Aiming at the need of air defense interception, the location principle of the least square method between the missile and the ground static station is analyzed based on the basic theory of the time difference and direction finding method. By comparing multiple location methods of missile and ground station co-location, a kind of technology is proposed to improve the tracking accuracy of the interference sources. A set of simulations are implemented to compare the accuracies of different location algorithms.