Classification of LPI radar signals using spectral correlation and support vector machines

Vanhoy, Garrett; Schucker, Thomas; Bose, T.

doi:10.1007/s10470-017-0944-0

Cited by 22 publications

(16 citation statements)

References 8 publications

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“…Compared with image processing algorithms, statistical signal recognition methods need to manually find and extract features, which is not as superior as deep learning algorithms (DLA) to directly extract features from images. Therefore, in the experimental comparison, the recognition probability of the algorithm proposed in this paper is also slightly weaker than [13,15]. However, the algorithm proposed in this paper has a smaller training set than DPL, and the algorithm is easier to implement.…”

Section: Recognitionmentioning

confidence: 92%

“…Dataset Method Overall SRP Lunden J et al [11] 9 kinds of LPI radar waveforms Time-Frequency analysis+ multilayer perceptron 76.5% Zhang M et al [12] 9 kinds of LPI radar waveforms Time-Frequency analysis + Elman neural network 93.8% Qu, Z et al [13] 12 kinds of LPI radar waveforms Time-frequency analysis + Convolutional neural network 98.9% Vanhoy, G et al [14] 12 kinds of LPI radar waveforms Spectral correlation + SVM 88.7% Hoang, L. M. et al [15] 12…”

Section: Table 3 Comparison Between Proposed Technique and Related Smentioning

confidence: 99%

“…By using CNN, the overall recognition probability is 92% at an SNR of -6dB. In [15], automatic LPI radar signal recognition technology based on spectral correlation density (SCD) is elaborated. The obtained signal SCD is performed to classify the signal features using a support vector machine (SVM), and the classification performance is 78% at a SNR of -2dB.…”

Section: Introductionmentioning

confidence: 99%

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Automatic LPI Radar Signal Sensing Method Using Visibility Graphs

et al. 2020

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The issue of the low probability of intercept (LPI) radar signal sensing has received considerable attention. Furthermore, the development of military technology further increased demand for it in future electronic warfare (EW). Meanwhile, so far, a systematic understanding of how radar signal detection and recognition technology contributes to EW is still lacking. Therefore, this study aims to contribute to this growing area of research by exploring an automatic method for detecting and identifying radar signals based on visibility graphs (VG), which can extract more network and feature information in the two-dimensional space of VG. In this paper, the signal to be measured is subjected to wavelet noise reduction. Secondly, auto-correlation processing is performed on the signal, we subsequently convert the signal into a VG complex network. Then the average degree and weighted operation of the network are used for signal detection and recognition, respectively. Experiments in the last part indicate that the proposed method provides excellent performance, such as the robustness to noise and the probability of classification, over several state-of-the-art algorithms.

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Section: Recognitionmentioning

confidence: 92%

Section: Table 3 Comparison Between Proposed Technique and Related Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic LPI Radar Signal Sensing Method Using Visibility Graphs

et al. 2020

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“…The other parameters of the samples are listed in Table 4. In the SVM classifier [27], the radial basis function (RBF) was selected as the kernel function; the parameter of the kernel function was 0.1 γ = . The penalty coefficient was C = 8.…”

Section: Performance Evaluation Of Experimentsmentioning

confidence: 99%

Radar Signal Sorting Method Based on Radar Coherent Characteristic

et al. 2020

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Aiming at the problem of reliability reduction of signal sorting in terms of the traditional five parameters and intrapulse feature in a complex electromagnetic environment, a new signal sorting method based on radar coherent characteristics is proposed. The main idea of this method is using spectrum analysis to obtain the spectrum images of coherent and noncoherent signals. Image-processing technology is used to extract the feature difference between the two spectrum images, and the central-moment feature is introduced to describe this difference. Through simulation analysis, the feasibility of using the central-moment feature as the coherent feature for signal sorting was proved. In order to check the effectiveness of the proposed feature, a number of simulations were conducted to demonstrate the sorting capability in terms of the coherent feature. From the simulations, it can be seen that the proposed feature not only can be used as a new feature for signal sorting but also that it can be utilized as a supplement for five typical parameters and the intrapulse feature to improve the sorting accuracy rate. Simulations also showed the proposed method could achieve satisfactory sorting results in a low signal-to-noise ratio (SNR). When the SNR was 5 dB, the sorting accuracy rate could reach 98%.

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“…As a classification method proposed by Vapnik et al, support vector machine [35] is mainly applied in the field of pattern recognition and has many unique advantages in small-sample, nonlinear, and high-dimensional pattern recognition. In recent years, it has been successfully applied in image recognition [36], signal processing [37], gene map recognition [38], and benign and malignant tumor recognition [39], showing its advantages. Moreover, field data are characterized by extremely complex relationships and few records, which is suitable for classification prediction with support vector machines.…”

Section: Support Vector Machine Modelmentioning

confidence: 99%

Identification of the Thief Zone Using a Support Vector Machine Method

Guo

Liu

et al. 2019

Processes

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Waterflooding is less effective at expanding reservoir production due to interwell thief zones. The thief zones may form during high water cut periods in the case of interconnected injectors and producers or lead to a total loss of injector fluid. We propose to identify the thief zone by using a support vector machine method. Considering the geological factors and development factors of the formation of the thief zone, the signal-to-noise ratio and correlation analysis method were used to select the relevant evaluation indices of the thief zone. The selected evaluation indices of the thief zone were taken as the input of the support vector machine model, and the corresponding recognition results of the thief zone were taken as the output of the support vector machine model. Through the training and learning of sample sets, the response relationship between thief zone and evaluation indices was determined. This method was used to identify 82 well groups in M oilfield, and the identification results were verified by a tracer monitoring method. The total identification accuracy was 89.02%, the positive sample identification accuracy was 92%, and the negative sample identification accuracy was 84.375%. The identification method easily obtains data, is easy to operate, has high identification accuracy, and can provide certain reference value for the formulation of profile control and water shutoff schemes in high water cut periods of oil reservoirs.

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Classification of LPI radar signals using spectral correlation and support vector machines

Cited by 22 publications

References 8 publications

Automatic LPI Radar Signal Sensing Method Using Visibility Graphs

Automatic LPI Radar Signal Sensing Method Using Visibility Graphs

Radar Signal Sorting Method Based on Radar Coherent Characteristic

Identification of the Thief Zone Using a Support Vector Machine Method

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