scholarly journals A Pulse Signal Characteristic Recognition Algorithm Based on Multifractal Dimension

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Yi-bing Li ◽  
Wei Nie ◽  
Fang Ye ◽  
Jing-chao Li
Keyword(s):  
Radiation Source ◽  
Impulse Noise ◽  
Fractal Theory ◽  
Pulse Signal ◽  
Recognition Algorithm ◽  
Transient Characteristics ◽  
Signal Characteristic ◽  
Low Snr

In low SNR condition, it is difficult to identify the radio transient characteristics of the signals. To solve this problem, a new recognition algorithm based on multifractal dimension characteristics is proposed. In fractal theory, multifractal dimension is the most sophisticated characterize that can describe the similar characteristics of the signals. Therefore, multifractal dimension is used in this paper to extract the subtle features of different impulse noise signals, in order to achieve the purpose of the classification and identification of the radiation source.

scholarly journals A Robust Real-Time Automatic Recognition Prototype for Maritime Optical Morse-Based Communication Employing Modified Clustering Algorithm

2020 ◽  
Vol 10 (4) ◽  
pp. 1227 ◽  
Author(s):  
Xiaozheng Wang ◽  
Minglun Zhang ◽  
Hongyu Zhou ◽  
Xinglong Lin ◽  
Xiaomin Ren
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Signal To Noise Ratio ◽  
Light Emitting Diode ◽  
Experimental Tests ◽  
Recognition Algorithm ◽  
Automatic Recognition ◽  
Decision Threshold ◽  
Low Snr ◽  
Led Array

In maritime communications, the ubiquitous Morse lamp on ships plays a significant role as one of the most common backups to radio or satellites just in case. Despite the advantages of its simplicity and efficiency, the requirement of trained operators proficient in Morse code and maintaining stable sending speed pose a key challenge to this traditional manual signaling manner. To overcome these problems, an automatic system is needed to provide a partial substitute for human effort. However, few works have focused on studying an automatic recognition scheme of maritime manually sent-like optical Morse signals. To this end, this paper makes the first attempt to design and implement a robust real-time automatic recognition prototype for onboard Morse lamps. A modified k-means clustering algorithm of machine learning is proposed to optimize the decision threshold and identify elements in Morse light signals. A systematic framework and detailed recognition algorithm procedure are presented. The feasibility of the proposed system is verified via experimental tests using a light-emitting diode (LED) array, self-designed receiver module, and microcontroller unit (MCU). Experimental results indicate that over 99% of real-time recognition accuracy is realized with a signal-to-noise ratio (SNR) greater than 5 dB, and the system can achieve good robustness under conditions with low SNR.

scholarly journals An Intra-Pulse Modulation Type Recognition Algorithm for Radar Signals Based on the Improved Residual Network

2021 ◽  
Vol 2050 (1) ◽  
pp. 012009
Author(s):  
Fan Wang ◽  
Yifeng Huang ◽  
Ming Zhu ◽  
Jun Tang ◽  
Zhaohong Jia
Keyword(s):  
Loss Function ◽  
Pulse Signal ◽  
Recognition Algorithm ◽  
Radar Signal ◽  
Residual Network ◽  
Pulse Modulation ◽  
Time Frequency ◽  
Signal Features ◽  
Modulation Type ◽  
Radar Signals

Abstract For purpose of solve the problem of poor discrimination and robustness of intra-pulse signal features extracted by the traditional methods, this paper proposes a radar signal intra-pulse modulation type recognition algorithm based on the improved residual network. Firstly, one-dimensional time-domain radar signal is converted into two-dimensional time-frequency image by Smoothing Pseudo Wigner-Ville Distribution; Then the time-frequency image is preprocessed; ResNet-50 network is chosen as the framework. In order to retain the feature map information as much as possible, the convolution kernel is increased in the residual module. The cross entropy loss function and the center loss function are used as the loss function to speed up the convergence of the network. The improved residual network is used to realize the intra-pulse modulation type recognition of radar signal. The simulation experiments show that when the SNR is -14dB, the overall average recognition accuracy of the improved algorithm for eight kinds of radar signals (CM, LFM, NLFM, BLFM, BPSK, QPSK, OPSK, LFM+BPSK) can reach 97.29%, which shows the effectiveness.

scholarly journals ANALYSIS OF RADIO SIGNAL PARAMETERS FOR EMISSION SOURCE IDENTIFICATION

Doklady BGUIR ◽  
2020 ◽  
pp. 52-58 ◽  
Author(s):  
D. V. Arkhipenkov
Keyword(s):  
Radiation Source ◽  
Rapid Determination ◽  
Source Parameters ◽  
Digital Signal ◽  
Recognition Algorithm ◽  
Detection Accuracy ◽  
Unknown Parameters ◽  
Coverage Area ◽  
Signal Parameters

The purpose of the article is the need to create a single portrait of a radioemission source and identification methods. Radiomonitoring tools are used to detect, identify and locate sources of radioemission in the coverage area. One of the important tasks solved by the radio monitoring system is the reception (interception) of transmitted messages on the air and signal identification. The article deals with the classification of the main parameters of radioemission sources, provides a classification of the modulation types and the main its parameters. The signal structure can be determined by autocorrelation and correlation methods. Autocorrelation is used to determine signal parameters such as the transmission duration, data block duration. Correlation allows to identify a specific signal from the set. To detect a radioemission source, two generalized algorithms are presented: recognition of the radioemission source type by unknown parameters and an algorithm for identifying a radiation source by given parameters. A simulation result of a radioemission source recognition algorithm with given parameters is presented; a linear frequencymodulated signature was used as a given signal. The result of the algorithm is a single outlier with full signal compliance, when the signals diverge, the outlier width increases, which indicates a discrepancy. This algorithm can be used to search for a given type of signal, which allows to increase the strip analysis speed and the detection accuracy. To increase the detection accuracy, it is recommended to use a combination of two algorithms with additional digital signal processing, which should lead to an increase in the accuracy of type of signal determining and a more rapid determination of the radiation source parameters.

scholarly journals Recognition of Impulse of Love at First Sight Based On Photoplethysmography Signal

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6572
Author(s):  
Huan Lu ◽  
Guangjie Yuan ◽  
Jin Zhang ◽  
Guangyuan Liu
Keyword(s):  
Feature Selection ◽  
Pulse Signal ◽  
Recognition Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Signal Acquisition ◽  
Interesting Phenomenon ◽  
Linear Discriminant ◽  
Time Frequency ◽  
Gradient Enhancement

Love at first sight is a well-known and interesting phenomenon, and denotes the strong attraction to a person of the opposite sex when first meeting. As far as we know, there are no studies on the changes in physiological signals between the opposite sexes when this phenomenon occurs. Although privacy is involved, knowing how attractive a partner is may be beneficial to building a future relationship in an open society where both men and women accept each other. Therefore, this study adopts the photoplethysmography (PPG) signal acquisition method (already applied in wearable devices) to collect signals that are beneficial for utilizing the results of the analysis. In particular, this study proposes a love pulse signal recognition algorithm based on a PPG signal. First, given the high correlation between the impulse signals of love at first sight and those for physical attractiveness, photos of people with different levels of attractiveness are used to induce real emotions. Then, the PPG signal is analyzed in the time, frequency, and nonlinear domains, respectively, in order to extract its physiological characteristics. Finally, we propose the use of a variety of machine learning techniques (support vector machine (SVM), random forest (RF), linear discriminant analysis (LDA), and extreme gradient enhancement (XGBoost)) for identifying the impulsive states of love, with or without feature selection. The results show that the XGBoost classifier has the highest classification accuracy (71.09%) when using the feature selection.

Joint Estimation of Doppler and Time Delay of Wideband Echoes for LFM Pulse Radar Based on Fractional Correlation in Impulse Noise Environment

2014 ◽  
Vol 989-994 ◽  
pp. 3706-3709
Author(s):  
Li Li
Keyword(s):  
Time Delay ◽  
Lower Order ◽  
Impulse Noise ◽  
Time Delay Estimation ◽  
Joint Estimation ◽  
New Method ◽  
Single Frequency ◽  
Noise Environment ◽  
Low Snr ◽  
Energy Cumulation

A new method to estimate the Doppler and multipath time delay is presented in impulse noise environment. First, the Doppler is estimated by energy cumulation of multipath component based on fractional lower-order-frational correlation transform. Then, an order-reduced signal is reconstructed combining the Doppler with the prior knowledge of the transmitted signal, and the echoes signal is converted to many single-frequency signals. Finally, the multipath time delay is obtained by the fractional lower-order power spectrum method. The method is adapted for low SNR noise, can restrain the affection of the cross-items, and has a high time-delay estimation resolution. Some computer simulations are given in this paper and the results show that the new method is valid.

Joint Estimation of Doppler and Multipath Time Delay of Wideband Echoes for LFM Pulse Radar Based on Fractional Correlation

2014 ◽  
Vol 543-547 ◽  
pp. 2400-2403
Author(s):  
Li Li
Keyword(s):  
Time Delay ◽  
Pulse Signal ◽  
Time Delay Estimation ◽  
Joint Estimation ◽  
New Method ◽  
Delay Estimation ◽  
Single Frequency ◽  
Low Snr ◽  
Pulse Radar ◽  
Energy Cumulation

A new method based on fractional correlation theory for estimating the Doppler and multipath time delay of the wideband echoes for LFM pulse radar by only one pulse signal is presented. First, the Doppler is estimated by energy cumulation of multipath component based on fractional correlation transform. Then, an order-reduced signal is reconstructed combining the Doppler with the prior knowledge of the transmitted signal, and the echoes signal is converted to many single-frequency signals. Finally, the multipath time delay is obtained by the dechirping method. The method is adapted for low SNR noise, can restrain the affection of the cross-items, and has a high time-delay estimation resolution. Some computer simulations are given in this paper and the results show that the new method is valid.

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