Aiming at the problems that the radar tracking and occupies data processing resources are affected by the residual clutter after target detection,a radar clutter suppression method based on genetic algorithm (GA) optimized back propagation (BP) neural network is proposed.By analyzing the differentiated features of radar target plots and clutter plots,multi-dimensional features are selected as input independent variables,and BP neural network classifier model is designed to classify radar target plots and clutter plots,so as to filter clutter.During the data input, genetic algorithm is used to optimize the input independent variables of neural network to reduce the dimension of input data and shorten the modeling time.During the neural network training process,GA is used to optimize the initial weights and thresholds of BP neural network to improve the network convergence speed and recognition accuracy.Tests on radar measured data show that:the BP neural network optimized by genetic algorithm has a 1.5% increase in the recognition rate of radar clutter plots and a 20.4% reduction in recognition time compared with the traditional BP neural network.

In view of the factors affecting the supportability of radar equipment are numerous and hierarchical, according to the requirement of radar equipment supportability, an index system of supportability evaluation of radar equipment is constructed. The comprehensive adjustment method is used to adjust the initial comprehensive value, so as to improve the problem that the overall comprehensive evaluation value in the catastrophe progression method is close to one, thus more intuitively reflect the size and level of the evaluation value. A supportability evaluation model of radar equipment based on improved catastrophe progression method is designed. The example shows that this method can evaluate the supportability level of the radar equipment scientifically and effectively.