Objective

2

In complex electronic warfare

radar emitter identification is an essential component of electronic intelligence and support systems; its related technology remains a critical factor in measuring the level of electronic countermeasure equipment technology. Radar emitter identification refers to extracting signal characteristics and then inputting the features into the classifier for identification. With the improvement of electronic technology

various jamming techniques have been applied to radar

making the identification of individual signal differences difficult. In addition

There are many types of radar signals

various modulation methods

and wide frequency coverage. Small individual feature differences between radar signals. There are a lot of noise

clutter and multipath interference in signals. Researchers mainly conduct the following two aspects

one is to extract the effective individual characteristics of the signal; the other is to optimize the classifier. Extracting signal discriminability characteristics using traditional radiation source identification techniques

such as template matching

classifier design

and decision matching

is challenging. Radar source identification technology is developing in the field of artificial intelligence. In cognitive ability

radiation source identification technology has a lot of room for development in the intelligent field. In the face of complex and diverse radar radiation source signals

existing radar radiation source identification algorithms are no longer able to cope with dense radar radiation source identification tasks. Given the robust data analysis capabilities of deep learning

convolutional neural network (CNN) are among the earliest and most widely used deep learning models. CNN has been used in radar source identification. In general

a CNN consists of the convolutional

pooling

activation

and fully connected layers. The convolutional layer and the layer stack structure extract powerful features and different features of data

respectively. The activation layer is used to enhance the feature expression ability of a network. The pooling layer can reduce dimensions and sparse feature layers. Feature combination and classification are performed at the full connection layer. In accordance with the characteristics of the radar radiation source signal

we propose a new radar source identification method based on CNN.

Method

2

Firstly

the data pre-processing unit is used to reduce the interference of noise on the signal. Secondly

the obtained signals are first extracted from their different domain features

and then the training set test set is divided. The third step is to design a convolutional neural network to extract and classify the extracted signals. At last

evaluate the performance of the method using test samples. The proposed method realizes the accurate identification of radar radiation source

which can fully mine the deep individual information of the radiation source signal. To extract the individual implicit features of the radiation source signal

our CNN has five layers; the first three are convolutional and the remaining two are fully connected layers. The kernel of the third convolutional layer and the pooling layer is set to 1D. Rectified linear unit (ReLU) nonlinearity is applied to the output of every convolutional and fully connected layer. ReLU improves network non-linearity. We use dropout

which can prevent overfitting

in the first two fully connected layers. The role of dropout is to randomly inactivate some neurons. The first convolutional layer filters with 36 kernels of size 3 with a stride of 1. The second convolution layer is consistent with the parameters of the first convolutional layer. The third convolutional layer has 64 kernels of size 5 with a stride of 1. The specific steps of the algorithm are as follows. First

the original radar data are preprocessed

i.e.

signal noise reduction and normalization. Second

we extract different characteristics of the signal. Lastly

the CNN is trained using different features.

Result

2

The training set ratios are 20%

40%

60%

and 80% of the total number of samples. This study compares the recognition accuracy of CNN when the input is a different feature and compares the recognition accuracy of the support vector machine (SVM)

extreme learning machine (ELM)

and depth Q network (DQN) models in deep reinforcement learning. Experiments show that the input of the network is different domain characteristics when the training set ratio is 80%; a high recognition rate can be obtained. Recognition accuracy rate reaches 100% and 99% for the spectral and fuzzy function slice features

respectively. When the input is the frequency domain feature with 80% of the training set

we compare the performance of SVM. Our method outperforms SVM. In particular

it improves accuracy by 0.9%. When the input is the fuzzy function slice feature

the accuracy rate of our method is improved by 16.13% and 1.87% compared with the SVM and ELM classifiers

respectively. Compared with the current popular recognition algorithm DQN

the improvement is 0.15%. In the experiment

when the input is a fuzzy function slice feature and the frequency offset values are 3 and 5

the four classifiers all obtain good recognition results. In particular

the method proposed in this paper has the highest recognition accuracy. It shows that choosing the optimal "near-zero" slice is helpful for the identification of radar radiation source.

Conclusion

2

The CNN designed in this study exhibits a strong feature expression ability. The experiment results show that our proposed method extracts the implicit features of signal discrimination and obtains a stable recognition rate. This method simplifies the network structure and requires less experience and hyperparameters in the same situation. It can improve the recognition accuracy of radar emitters.