To solve the problem of sensor optimal deployment in the constrained conditions, a multi-sensor optimal deployment method is proposed based on genetic algorithm particle swarm optimization (GA-PSO). The method firstly meshes the battlefield geography environment and establishes the constraint matrix of deployment according to the battlefield geography environment and tactical conditions. Then, the objective optimization function based on detection coverage is established. Finally, the GA-PSO is used to solve the optimal position of the sensor. The simulation verifies the effectiveness and rationality of the proposed method.

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.

The principle of future airspace window shooting system is introduced, and a new projectile dispersion center configuration method, i.e. the diamond distribution method is proposed. The damage probability of airspace window shooting is given, and the effect indexes of the configuration methods are analyzed according to the mean and the uniformity of damage probability. The simulation results show that the diamond distribution method increases the target damage probability and is suitable for configuring the projectile dispersion center of future airspace window.