Research on Modulation Recognition Algorithm of Digital Communication Signal Based on Wavelet Denoising

2014 ◽  
Vol 608-609 ◽  
pp. 459-467 ◽  
Author(s):  
Xiao Yu Gu
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Decision Theory ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Modulation Recognition ◽  
The Core ◽  
Feature Parameters ◽  
Communication Signal ◽  
Modulation Signal

The paper researches a recognition algorithm of modulation signal and modulation modes. The modulation modes to be recognized include 2ASK, 2FSK, 2PSK, 4ASK, 4FSK and 4PSK modulation. There are two methods recognizing modulation modes of digital signal, method based on decision theory and pattern-recognition method based on feature extraction. The method based on decision theory is not suitable for recognition with multiple modulation modes. The core of pattern recognition based on feature extraction is selection of feature parameters. So the paper uses the feature parameters with simple calculation, easy to be implemented and high recognition rate as the core. The extraction of feature parameters is based on instant feature of modulation signal after Hilbert transformation.

Analysis and Research of Modulation Recognition Algorithm in Satellite Communications

2013 ◽  
Vol 756-759 ◽  
pp. 1961-1967
Author(s):  
Ren Bin ◽  
Zhu Ping ◽  
Cheng Ying ◽  
Song Lei
Keyword(s):  
Software Defined Radio ◽  
Satellite Communication ◽  
Recognition Rate ◽  
Signal Amplitude ◽  
Satellite Communications ◽  
Recognition Algorithm ◽  
Automatic Identification ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Communication Signal

Automatic identification of the communication signal modulation mode is a hot topic of research in the signal processing field in recent years. It is an important part of electronic countermeasures, and also a rapidly developed field of signal analysis. Communication signal modulation recognition is widely used in signal confirmation, interference identification, radio listening, signal monitoring, as well as software-defined radio, satellite communications and other fields. This paper proposed a digital modulation signal automatic recognition algorithm for satellite communication applications,. The algorithm uses the higher order cumulants of modulating signal, combined with constellation map features, to classify the signal. It has invariance in signal amplitude and phase deviation, and inhibits the additive Gaussian noise at the same time. Compared with other identification algorithms, it has the characteristics of stability and wide application. Computer simulations show that, under the given data length and moderate SNR conditions, a higher recognition rate (> 95%) can be obtained.

Research on Applied Technology of Palmprint Recognition Based on Fisher Linear Discriminant and Improved PCA Algorithm

2014 ◽  
Vol 886 ◽  
pp. 515-518
Author(s):  
Jing Wen Li
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Critical Conditions ◽  
Palmprint Recognition ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Biometric Technology ◽  
Applied Technology ◽  
Pca Algorithm

The information applied technology of palmprint recognition is a biometric technology, it’s based on the effective information on the palm (such as: palmprint) to identifies people. The palmprint is unique and characteristic, these are the identification of critical conditions. The feature extraction of palmprint image is a prerequisite for recognition, feature extraction algorithm depends on the quality of the recognition rate and efficiency. This paper presents a method of palmprint recognition algorithm based on Fisher linear discriminant analysis and improved PCA algorithm. The experimental results show that, the recognition rate is improved.

Preprocessing and Feature Extraction, Coding, Matching Algorithm for Fingerprint Image

2013 ◽  
Vol 805-806 ◽  
pp. 1900-1906
Author(s):  
He Ping Jia
Keyword(s):  
Feature Extraction ◽  
Gabor Filter ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Fingerprint Image ◽  
Development Platform ◽  
Traditional Algorithm ◽  
Fingerprint Image Enhancement ◽  
Global And Local

A set of fingerprint recognition algorithm was achieved mainly including Gamma controller normalization and equalizing, fingerprint image division, fingerprint image binarization and different direction Gabor filter for feature extraction; especially Fingerprint image enhancement and the textures based on Gabor filter, taking account of both global and local features of the fingerprints.using matlab 7.0 for development platform was verified,The experimental results showed the proposed algorithm can avoid all sorts of false characters more effectively and recognition rate is higher than traditional algorithm in the same conditions.

Application of ART-2 Neural Network and Invariant Moment in Image Pattern Recognition

2012 ◽  
Vol 433-440 ◽  
pp. 4014-4019 ◽  
Author(s):  
Lei Hao ◽  
Yue Hua Gao ◽  
Rui Jun Jia
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Recognition Rate ◽  
Experimental Results ◽  
Invariant Moment ◽  
Recognition Model ◽  
Image Pattern

This paper mainly uses image pre-processing and feature extraction to calculate the invariant moment of image, and ultimately realizes the image pattern recognition based on ART-2 neural network. Experimental results show that ART-2 neural network has high recognition rate. It also solves the contradiction between network's plasticity and stability, when new recognition model appears.

scholarly journals Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xinfeng Ge ◽  
Jing Zhang ◽  
Ye Zhou ◽  
Jianguo Cai ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Hybrid Model ◽  
Rough Set ◽  
Recognition Rate ◽  
Zernike Moment ◽  
Calculation Error ◽  
Time Step

In the shaft axis monitoring of hydrogenerating unit condition monitoring and fault diagnosis, the shaft orbit is intuitive and comprehensively reflects the unit operation state, and different shaft orbits correspond to different fault types, which can accurately indicate a system vibration fault. Shaft orbit identification has important significance for vibration fault diagnosis. In getting the feature extraction and pattern recognition of a shaft orbit, the Zernike moment is better than the Hu moment; it has the advantages of a small calculation error and a high recognition rate. A rough set neural network (RS-BP hybrid model) of shaft orbit recognition is established, which uses just 13 moment eigenvalues reserved by the rough set feature selection algorithm as input variables; it has the same calculation error and recognition rate and reduces the calculation time step. The simulation of the recognition of shaft orbits shows that the hybrid model has achieved good results in the identification of shaft orbits.

scholarly journals A Novel Digital Modulation Recognition Algorithm Based on Deep Convolutional Neural Network

2020 ◽  
Vol 10 (3) ◽  
pp. 1166 ◽  
Author(s):  
Kaiyuan Jiang ◽  
Jiawei Zhang ◽  
Haibin Wu ◽  
Aili Wang ◽  
Yuji Iwahori
Keyword(s):  
Phase Shift ◽  
Rapid Development ◽  
Recognition Rate ◽  
Digital Signal ◽  
Recognition Algorithm ◽  
Svm Classifier ◽  
Phase Shift Keying ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Shift Keying

The modulation recognition of digital signals under non-cooperative conditions is one of the important research contents here. With the rapid development of artificial intelligence technology, deep learning theory is also increasingly being applied to the field of modulation recognition. In this paper, a novel digital signal modulation recognition algorithm is proposed, which has combined the InceptionResNetV2 network with transfer adaptation, called InceptionResnetV2-TA. Firstly, the received signal is preprocessed and generated the constellation diagram. Then, the constellation diagram is used as the input of the InceptionResNetV2 network to identify different kinds of signals. Transfer adaptation is used for feature extraction and SVM classifier is used to identify the modulation mode of digital signal. The constellation diagram of three typical signals, including Binary Phase Shift Keying(BPSK), Quadrature Phase Shift Keying(QPSK) and 8 Phase Shift Keying(8PSK), was made for the experiments. When the signal-to-noise ratio(SNR) is 4dB, the recognition rates of BPSK, QPSK and 8PSK are respectively 1.0, 0.9966 and 0.9633 obtained by InceptionResnetV2-TA, and at the same time, the recognition rate can be 3% higher than other algorithms. Compared with the traditional modulation recognition algorithms, the experimental results show that the proposed algorithm in this paper has a higher accuracy rate for digital signal modulation recognition at low SNR.

P-Box Theory and SVM Methods with Application in Pattern Recognition

2014 ◽  
Vol 651-653 ◽  
pp. 472-475
Author(s):  
Jia Man Ding ◽  
Yi Du ◽  
Qing Xin Wang ◽  
Ying Jiang ◽  
Lian Yin Jia
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Information Source ◽  
Recognition Rate ◽  
Extraction Process ◽  
Information Loss ◽  
New Method ◽  
Uncertainty Measures ◽  
Different Types ◽  
Traditional Pattern

In order to solve the problem of the information loss on the feature extraction process in the traditional pattern recognition, a new method based on probability boxes theory was proposed. Firstly, the skewness of the fault signal data were used as the information source to construct the tow p-boxes about X and direction. Then, to take advantage of the complementation of the information source, the tow p-boxes from different directions were fused. Finally, the SVM features database was established by extracting different types of cumulative uncertainty measures from p-boxes. The analysis result shows that the combination of p-box and SVM can achieve a high recognition rate, which makes a new way for pattern recognition.

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