Radar Waveform Recognition Based on Multiple Autocorrelation Images

Huang, Zhi Gao; Ma, Zhiyuan; Huang, Gan

doi:10.1109/access.2019.2930250

Cited by 15 publications

(14 citation statements)

References 18 publications

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“…It is worth noting that, double short-time autocorrelation processing can be used on each frame sequence, which have been short-time autocorrelated, in order to enhance the classification effect of the classification network. Our previous research explained the calculation method of double and multiple autocorrelation [25]. For the specific implementation of double short-time autocorrelation, the input is also a signal, and then the signal is short-time autocorrelated.…”

Section: Proposed Noise Reduction Algorithm For Lpi Signalmentioning

confidence: 99%

“…For the TFI that is generated by adaptive filtering combined with various LMS algorithms [23,24], the noise component will completely overwhelm the signal component. In [25], the feature image construction algorithm can perform autocorrelation processing on only six types of conventional radar signals (CP, LFM, NCPM, BPSK, BFSK, QFSK), but it is not applicable to non-stationary signals, such as Costas, LFM, P1-P4, T1-T4, and Frank. For the second method, literature [6,12,13], and [16] reduce the TFI noise through image processing, but the processing methods of image morphology and threshold filtering will easily lose the signal components.…”

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confidence: 99%

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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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Section: Proposed Noise Reduction Algorithm For Lpi Signalmentioning

confidence: 99%

mentioning

confidence: 99%

LPI Radar Waveform Recognition Based on Features from Multiple Images

Huang

Lin

2020

Sensors

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“…Various techniques have been proposed for classification by using Time-Frequency Images. As a LPI Radar Waveform Recognition Technique [3], [6] uses the Convolutional Neural Network (CNN). The computational cost has become a problem because of the generated TFI sizes.…”

Section: Introductionmentioning

confidence: 99%

Lpi Radar Waveform Classification Using Binary SVM and Multi-Class SVM Based on Principal Components of Tfi

Bektaş

Ergezer

2020

Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng.

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Since cognition has become an important topic in Electronic Warfare (EW) systems, Electronic Support Measures (ESM) are used to monitor, intercept and analyse radar signals. Low Probability of Intercept (LPI) radars is preferred to be able to detect targets without being detected by ES systems. Because of their properties as low power, variable frequency, wide bandwidth, LPI Radar waveforms are difficult to intercept with ESM systems. In addition to intercepting, the determination of the waveform types used by the LPI Radars is also very important for applying counter-measures against these radars. In this study, a solution for the LPI Radar waveform recognition is proposed. The solution is based on the training of Support Vector Machine (SVM) after applying Principal Component Analysis (PCA) to the data obtained by Time-Frequency Images (TFI). TFIs are generated using Choi-Williams Distribution. High energy regions on these images are cropped automatically and then resized to obtain uniform data set. To obtain the best result in SVM, the SVM Hyper-Parameters are also optimized. Results are obtained by using one-against-all and one-against-one methods. Better classification performance than those given in the literature have been obtained especially for lower Signal to Noise Ratio (SNR) values. The cross-validated results obtained are compared with the best results in the literature.

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“…Singular value decomposition was used to decompose the time-frequency matrices of a signal into noise and signal subspaces to reduce the influence of noise [17]. The threshold value of a time-frequency matrix was defined to remove noise as much as possible through threshold filtering [18]. IM and threshold filtering were combined to reduce noise in TFI [19].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, for the two-dimensional noise reduction algorithm, the overall recognition rate of the de-noised signal in [7] is less than 65% in the SNR of -9 dB. The way of reducing time-frequency domain noise by image processing can reduce the noise of TFI [16,18], [19], however, the processing of IM and threshold filtering tends to lose the signal components contained in TFI. The noise-reduction effect for singular value decomposition was simulated at SNR > 0 dB [17], while simulations were not conducted for SNR < 0 dB.…”

Section: Introductionmentioning

confidence: 99%

Emitter Signal Waveform Classification Based on Autocorrelation and Time-Frequency Analysis

Huang

Lin

2019

Electronics

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Emitter signal waveform recognition and classification are necessary survival techniques in electronic warfare systems. The emitters use various techniques for power management and complex intra-pulse modulations, which can create what looks like a noisy signal to an intercept receiver, so emitter signal waveform recognition at a low signal-to-noise ratio (SNR) has gained increased attention. In this study, we propose an autocorrelation feature image construction technique (ACFICT) combined with a convolutional neural network (CNN) to maintain the unique feature of each signal, and a structure optimization for CNN input layer called hybrid model is designed to achieve image enhancement of the signal autocorrelation, which is different from using a single image combined with CNN to complete classification. We demonstrate the performance of ACFICT by comparing feature images generated by different signal pre-processing algorithms, and the evaluation indicators are signal recognition rate, image stability degree, and image restoration degree. This paper simulates six types of the signals by combining ACFICT with three types of hybrid model, the simulation results compared with the literature show that the proposed methods not only has a high universality, but also better adapts to waveform recognition at low SNR environment. When the SNR is –6 dB, the overall recognition rate of the method reaches 88%.

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Radar Waveform Recognition Based on Multiple Autocorrelation Images

Cited by 15 publications

References 18 publications

LPI Radar Waveform Recognition Based on Features from Multiple Images

LPI Radar Waveform Recognition Based on Features from Multiple Images

Lpi Radar Waveform Classification Using Binary SVM and Multi-Class SVM Based on Principal Components of Tfi

Emitter Signal Waveform Classification Based on Autocorrelation and Time-Frequency Analysis

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