A Radar Signal Sorting Method Based on Immune Evolutionary Artificial Neural Network

The sorting and feature extraction of radar signal are important precondition of the recognition and location to the radars in reconnaissance areas, and they are also an important piece of content in radar countermeasure reconnaissance information analysis. Radar signal sorting is to separate the pulse signals of each radar from the complex signal pulse data received by radar countermeasure scout, and serve as the base of subsequent feature extraction and signal recognition. For the environment of electromagnetism signal has become complicated day by day, so the conventional methods for signal sorting cannot satisfy the requirement of modern warfare. In order to settle this problem better, the sorting of radar signals by artificial neural network (ANN) based on immune evolutionary algorithms is realized in this paper. The ANN based on immune evolutionary algorithms uses global search optimizing means, overcomes the intrinsic shortcomings of conventional methods, and improves the convergence speed and performance of the network greatly. Computer simulation proves that the correct sorting rate of the immune evolutionary ANN is high when special radar signals is inputted.