Aiming at the difficulty of diagnosis caused by the complexity of equipment circuit, noise of working environment and big data of traditional intelligent fault diagnosis method, a fusion model based on noise reduction self-encoder and deep belief network is proposed to realize the fault diagnosis of analog circuit. The denoising autoencoder is used to process the random noise of the original signal and to learn the low-level features. The deep belief network extracts the deep features based on the learned low-level features. The fused depth features are incorporated into Softmax classifier for intelligent diagnosis model training. The new diagnostic model is validated on typical analog circuits. The results show that the proposed method has superior diagnostic performance.

The principle of future airspace window shooting system is introduced, and the effect of ideal future airspace window caused by the time and position prediction error when the target meets with future airspace window is considered. The multiple future airspace window configurations and three dimensional ellipsoidal zone of future airspace district configuration are put forward, and the kill probability of the configuration modes is analyzed. The simulation results show that the damage efficiency of ellipsoid domain of future airspace district configuration mode is the best, the multiple future airspace window configuration modes are the second, and the influence of the time and position prediction errors is reduced.