Radar signal intra-pulse modulation identification method

Abstract

The invention provides a radar signal intra-pulse modulation identification method, which comprises the following steps of performing Cohen type time-frequency distribution processing on an intercepted radar signal to obtain a time-frequency image, preprocessing the time-frequency image, namely adjusting the size and the amplitude, and then grouping and tagging to make a training set and a test set, designing a deep convolutional neural network, adjusting the size and the amplitude of a radar signal of an unknown modulation type, putting the radar signal into the trained deep convolutional neural network, and automatically judging the radar signal type by the network to complete identification. Compared with a kernel function in the Choi-Williams distribution, the time-frequency analysis kernel function provided by the invention has a better effect on inhibiting the cross terms of radar signals, and the signal robustness characteristic is more obvious. The classification network is pre-trained by using the convolutional de-noising auto-encoder, so that the information loss of signal energy caused by time-frequency image preprocessing can be avoided, the classification accuracy of the whole system is improved, and the method is simple to operate and is easier to implement.

Description

technical field [0001] The invention relates to a recognition method, in particular to a radar signal intrapulse modulation recognition method. Background technique [0002] Radar signal intrapulse modulation recognition is a key technology in radar electronic warfare, and plays an important role in modern electronic support systems, electronic intelligence systems and radar threat warning systems. With the continuous improvement of radar technology, pulse compression technology is widely used in radar, which makes the power spectral density of radar signal lower and lower, and the types of radar signal are more and more. Therefore, the radar signal intrapulse modulation recognition algorithm should not only have good recognition performance under low signal-to-noise ratio, but also be able to recognize a wide range of signal types. [0003] The traditional radar signal intrapulse modulation recognition algorithm can be divided into two parts: feature extraction and classif...

AI Technical Summary

Problems solved by technology

However, these recognition algorithms based on deep learning still have some problems: in order to remove the noise in the time-frequency image, too much image preprocessing is adopted, which leads to the loss of part of the effective information of the signal and reduces the recognition accuracy; The convolutional neural network of LeNet-5 will increase the number of parameters and increase the computational complexity when the depth increases.

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