Automatic Intrapulse Modulation Classification of Advanced LPI Radar Waveforms

Kishore, T. Ravi; Rao, K. Deergha

doi:10.1109/taes.2017.2667142

Cited by 139 publications

(80 citation statements)

References 20 publications

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“…It is one of the most effective anti-reconnaissance and anti-jamming technologies. Therefore, how to identify LPI radar signal waveform effectively becomes the focus of non-cooperative radar signal processing research [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

LPI Radar Waveform Recognition Based on Deep Convolutional Neural Network Transfer Learning

2019

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Low Probability of Intercept (LPI) radar waveform recognition is not only an important branch of the electronic reconnaissance field, but also an important means to obtain non-cooperative radar information. To solve the problems of LPI radar waveform recognition rate, difficult feature extraction and large number of samples needed, an automatic classification and recognition system based on Choi-Williams distribution (CWD) and depth convolution neural network migration learning is proposed in this paper. First, the system performs CWD time-frequency transform on the LPI radar waveform to obtain a 2-D time-frequency image. Then the system preprocesses the original time-frequency image. In addition, then the system sends the pre-processed image to the pre-training model (Inception-v3 or ResNet-152) of the deep convolution network for feature extraction. Finally, the extracted features are sent to a Support Vector Machine (SVM) classifier to realize offline training and online recognition of radar waveforms. The simulation results show that the overall recognition rate of the eight LPI radar signals (LFM, BPSK, Costas, Frank, and T1–T4) of the ResNet-152-SVM system reaches 97.8%, and the overall recognition rate of the Inception-v3-SVM system reaches 96.2% when the SNR is −2 dB.

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Section: Introductionmentioning

confidence: 99%

LPI Radar Waveform Recognition Based on Deep Convolutional Neural Network Transfer Learning

2019

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“…where C x (t, Ω) is the result of TFT [22], t and Ω are the time and frequency axis, respectively, and the scale factor σ is used to control the distribution of CWD cross-terms. The frequency resolution decreases when σ inhibits the cross-terms in CWD.…”

Section: Of 23mentioning

confidence: 99%

“…Researchers have dedicated their attention to image pre-processing, generally in two ways, in order to solve this problem. First, before time-frequency conversion, algorithm filtering is used to reduce noise [21][22][23][24]; second, some image processing technology is used to process TFI to achieve accurate classification [6,13,15,16]. For the first method, most of the current noise reduction algorithms are difficult to apply in a low SNR environment.…”

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confidence: 99%

LPI Radar Waveform Recognition Based on Features from Multiple Images

Huang

Lin

2020

Sensors

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Detecting and classifying the modulation type of the intercepted noisy LPI (low probability of intercept) radar signals in real-time is a necessary survival technique in the electronic intelligence systems. Most radar signals have been designed to have LPI properties; therefore, the LPI radar waveform recognition technique (LWRT) has recently gained increasing attention. In this paper, we propose a multiple feature images joint decision (MFIJD) model with two different feature extraction structures that fully extract the pixel feature to obtain the pre-classification results of each feature image for the non-stationary characteristics of most LPI radar signals. The core technology of this model is combining the short-time autocorrelation feature image, double short-time autocorrelation feature image and the original signal time-frequency image (TFI) simultaneously input into the hybrid model classifier, which is suitable for non-stationary signals, and it has higher universality. We demonstrate the performance of MFIJD by simulating 11 types of the signals defined in this paper and generating training sets and test sets. The comparison with the literature shows that the proposed methods not only has a high universality for LPI radar signals, but also better adapts to LPI radar waveform recognition at low SNR (signal to noise ratio) environment. The overall recognition rate of the method reaches 87.7% when the SNR is −6 dB.Sensors 2020, 20, 526 2 of 23 some existing feature extraction methods are highly targeted, mainly for some specific radar emitter signals. For another thing, these methods rarely involve problems with noise effects and low SNR. In fact, radar signals, especially LPI radar signals, are inevitably subject to large amounts of noise during propagation and reception [5], and LPI radars usually have lower power and it is difficult to directly classify [6]. In current research, there have been some LPI waveform recognition technologies (LWRT), which use feature extraction and classification techniques. Time-frequency analysis (TFA) is widely used in the feature extraction since LPI radar signals are usually non-stationary signals, such as Smoothed Pseudo-Wigner Distribution (SPWD) [7], Wigner Ville Distribution (WVD) [8], Short-Time Fourier Transform (STFT) [9][10][11], and Choi-Williams Distribution (CWD) [6,[12][13][14][15][16]. Combined with deep learning in the field of computer vision [17] and models of neural network structures, researchers have obtained better recognition results from the time-frequency feature of signals [18]. The radar signal is first time-frequency transformed into a two-dimensional time-frequency image (TFI), which then is preprocessed and sent to a neural network for training. In the area of classifier design, classification methods include multi-Layer Perceptron (MLP) [11], conditional decision for different features [11,15], Convolutional Neural Networks (CNN) [14], Elman Neural Networks (ENN) [6], and support vector machines (SVM) [6,16]. In addition, there have been hy...

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“…After the radar signals are detected by the transmitter, it should be separated and recognized in time. Then, the type, quantity, and threat level of threat radars can be analyzed by combining other pulse parameters, so that the corresponding measures can be taken to jam the radar effectively and protect the transmitter's information from being stolen …”

Section: Introductionmentioning

confidence: 99%

Modulation recognition for radar emitter signals based on convolutional neural network and fusion features

Gao

Shen

Gao

2019

Trans Emerging Tel Tech

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With the increase of radar signal modulations and the emergence of new system radars, the receiver will intercept radar signals at the same time. In order to accurately estimate and suppress the signals, this paper proposes an accurate recognition system for radar emitter signals. The system can effectively separate multiple signals and accurately recognize Binary Phase Shift Keying (BPSK), Linear Frequency Modulation (LFM), Continuous Wave (CW), Costas, Frank code, and P1 to P4 codes. The separation technique based on fractional Fourier transform is proposed to decompose received signals into multiple components.Furthermore, a transferable GoogleNet is explored to achieve accurate recognition of the first component with better separation effect. Meanwhile, variational mode decomposition is developed to eliminate the noise of the second component; then, the fusion features are extracted to improve the recognition rate of the second component. Finally, the improved particle swarm optimization algorithm is proposed to find best support vector machine parameters. The simulation results show that the recognition rate of single signal and double signals can reach 96.23% and 72%, respectively, when signal-to-noise ratio is 0 dB.The system can also bring some inspiration to medical and mechanical signal recognition.Trans Emerging Tel Tech. 2019;30:e3612.wileyonlinelibrary.com/journal/ett

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Automatic Intrapulse Modulation Classification of Advanced LPI Radar Waveforms

Cited by 139 publications

References 20 publications

LPI Radar Waveform Recognition Based on Deep Convolutional Neural Network Transfer Learning

LPI Radar Waveform Recognition Based on Deep Convolutional Neural Network Transfer Learning

LPI Radar Waveform Recognition Based on Features from Multiple Images

Modulation recognition for radar emitter signals based on convolutional neural network and fusion features

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