Modulation classification method for frequency modulation signals based on the time–frequency distribution and CNN

Zhang, Juan; Li, Yong; Ju, Youlun

doi:10.1049/iet-rsn.2017.0265

Cited by 44 publications

(41 citation statements)

References 30 publications

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“…Also, the timescale characteristics [24], the modulation domain [25], basic function neural networks [26], Rihaczek distribution and Hough transform [27], which is a pattern recognition technique invented in 1959 by Paul Hough, subject to a patent, and used in the processing of digital images. The simplest application can detect lines present in an image, but modifications can be made to this technique to detect other geometric shapes; it is the generalized Hough transform developed by Richard Duda and Peter Hart in 1972 [28][29][30], frequency estimation [28], pulse repetition interval [31], twodimensional bispectrum [32], etc.…”

Section: The Methods Of Classification Of Radar Signals Is Presented Inmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence Approach for Target Classification: A State of The Art

Abdellaoui¹,

Daghouj²,

Fattah³

et al. 2020

Adv. sci. technol. eng. syst. j.

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The classification of static or mobile objects, from a signal or an image containing information as to their structure or their form, constitutes a constant concern of specialists in the electronic field. The remarkable progress made in past years, particularly in the development of neural networks and artificial intelligence systems, has further accentuated this trend. The fields of application and potential uses of Artificial Intelligence are increasingly diverse: understanding of natural language, visual recognition, robotics, autonomous system, Machine learning, etc. This paper is a state of the art on the classification of radar signals. It focuses on the contribution of artificial intelligence to the latter without forgetting target tracking. This by evoking the different feature extractors, classifiers and the existing identification deep learning algorithms. We detail also the process allowing carrying out this classification.

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Section: The Methods Of Classification Of Radar Signals Is Presented Inmentioning

confidence: 99%

“…These methods of classification represent research in several disciplines [22,30,[33][34][35]. To allow proper operation in complex signal environments with many radar transmitters, signal classification should be able to handle not determined, corrupt, and equivocal measurements reliably.…”

Section: The Methods Of Classification Of Radar Signals Is Presented Inmentioning

confidence: 99%

Artificial Intelligence Approach for Target Classification: A State of The Art

Abdellaoui¹,

Daghouj²,

Fattah³

et al. 2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…Wavelet analysis is a local transform of time and frequency, which can effectively extract information from the signal and is conducive to the perception of the surrounding electromagnetic environment. For some time, many CR technologies are devoted to modulation recognition of communication signals by using spectrum and cyclic spectrum [2], characteristic parameters and their statistics [3], time-frequency transform [4], [5], and high-order cumulants [6]- [8]. However, these methods are difficult to achieve multiresolution analysis of the modulated signals, which increases the difficulty of ob-taining effective information, and the real-time performance of signal analysis and processing is not good.…”

Section: A Literature Reviewmentioning

confidence: 99%

Intelligent Modulation Pattern Recognition Based on Wavelet Approximate Coefficient Entropy in Cognitive Radio Networks

et al. 2020

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“…For the rich coverage of feature parameters and less parameter overlap, the in-pulse feature selected as the parameter has become a trend of radar waveform sorting technology in recent years [6] [12].To get the in-pulse features, some extraction such as Short-Time Fourier transform (STFT) [6] [10] Choi-Williams distribution (CWD) [7] and Wigner-Ville Distribution (WVD) [8] can get the time-frequency feature of radar emitter signals. For the extracted features in two-dimensions or multi-dimensions, some classification algorithm such as Support Vector Machine (SVM) [7] [12] CNN [8], Long Short-term Memory (LSTM) [9] Probabilistic Neural Network (PNN) [10] and Back Propagation Neuron Network (BPNN) [11] have been used to sort the radar waveform and achieved a high accuracy rate. However, for the classifiers in deep learning networks waveform signals, the high classification accuracy has achieved with cost of abundant of training data and training time.…”

Section: Introductionmentioning

confidence: 99%

Unknown Radar Waveform Recognition Based on Transferred Deep Learning

Lin

Huang

et al. 2020

IEEE Access

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Radar signals are emerging constantly for urgent task because of its complex patterns and rich working modes. For some radar waveforms with known modulation methods, they can be identified by correlation between radar prior knowledge and the received signals by the reconnaissance receiver. As for the unknown radar signals, how to identify unknown radar waveforms under the condition of limited samples and low signal-to-noise ratio is a challenging problem. Aiming at the learning ability of the deep features of the image by the convolutional neural network (CNN), the reconstructed features of the timefrequency image (TFI) of the known and unknown radar waveform signals have been excavated. A decision fusion unknown radar signal identification model based on transfer deep learning and linear weight decision fusion is designed in this paper. Firstly, the CNN is trained using the known radar signals; Then, based on the transfer learning, the neurons obtained from the multiple underlying the CNN are used to represent the reconstruction feature; Finally, the performance of the single random forest classifier of the original TFI and short-time autocorrelation features images (SAFI)are fused, the identification decision of unknown signals is realized by setting linear weight to the two databases. The recognition rate of unknown new classes for small samples exceeds 80.31%, and the classification accuracy rate for known radar waveform reach more than 99.15%. INDEX TERMS Unknown radar waveform recognition, convolutional neural network, decision fusion, transfer learning, random forest.

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Modulation classification method for frequency modulation signals based on the time–frequency distribution and CNN

Cited by 44 publications

References 30 publications

Artificial Intelligence Approach for Target Classification: A State of The Art

Artificial Intelligence Approach for Target Classification: A State of The Art

Intelligent Modulation Pattern Recognition Based on Wavelet Approximate Coefficient Entropy in Cognitive Radio Networks

Unknown Radar Waveform Recognition Based on Transferred Deep Learning

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