This paper presents a novel method for radar emitter signal recognition. First, wavelet packet transform （WPT） is introduced to extract features from radar emitter signals. Then, rough set theory is used to select t...This paper presents a novel method for radar emitter signal recognition. First, wavelet packet transform （WPT） is introduced to extract features from radar emitter signals. Then, rough set theory is used to select the optimal feature subset with good discriminability from original feature set, and support vector machines （SVMs） are employed to design classifiers. A large number of experimental results show that the proposed method achieves very high recognition rates for 9 radar emitter signals in a wide range of signal-to-noise rates, and proves a feasible and valid method.展开更多
The existing direction of arrival （DOA） estimation algorithms based on the electromagnetic vector sensors array barely deal with the coexisting of independent and coherent signals. A two-dimensional direction findin...The existing direction of arrival （DOA） estimation algorithms based on the electromagnetic vector sensors array barely deal with the coexisting of independent and coherent signals. A two-dimensional direction finding method using an L-shape electromagnetic vector sensors array is proposed. According to this method, the DOAs of the independent signals and the coherent signals are estimated separately, so that the array aperture can be exploited sufficiently. Firstly, the DOAs of the independent signals are estimated by the estimation of signal parameters via rotational invariance techniques, and the influence of the co- herent signals can be eliminated by utilizing the property of the coherent signals. Then the data covariance matrix containing the information of the coherent signals only is obtained by exploiting the Toeplitz property of the independent signals, and an improved polarimetric angular smoothing technique is proposed to de-correlate the coherent signals. This new method is more practical in actual signal environment than common DOA estimation algorithms and can expand the array aperture. Simulation results are presented to show the estimating performance of the proposed method.展开更多
Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. I...Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. In this paper, a novel training algorithm based on total least squares (TLS) for an LS-SVM is presented and applied to multifunctional sensor signal reconstruction. For three different nonlinearities of a multifunctional sensor model, the reconstruction accuracies of input signals are 0.001 36%, 0.031 84% and 0.504 80%, respectively. The experimental results demonstrate the higher reliability and accuracy of the proposed method for multifunctional sensor signal reconstruction than the original LS-SVM training algorithm, and verify the feasibility and stability of the proposed method.展开更多
When the drivers approaching signalized intersections(onset of yellow signal),the drivers would enter into a zone,where they will be in uncertain mode assessing their capabilities to stop or cross the intersection.The...When the drivers approaching signalized intersections(onset of yellow signal),the drivers would enter into a zone,where they will be in uncertain mode assessing their capabilities to stop or cross the intersection.Therefore,any improper decision might lead to a right-angle or back-end crash.To avoid a right-angle collision,drivers apply the harsh brakes to stop just before the signalized intersection.But this may lead to a back-end crash when the following driver encounters the former's sudden stopping decision.This situation gets multifaceted when the traffic is heterogeneous,containing various types of vehicles.In order to reduce this issue,this study's primary objective is to identify the driving behaviour at signalized intersections based on the driving features(parameters).The secondary objective is to classify the outcome of driving behaviour(safe stopping and unsafe stopping)at the signalized intersection using a support vector machine(SVM)technique.Turning moments are used to identify the zones and label them accordingly for further classification.The classification of 50 instances is identified for training and testing using a 70%-30% rule resulted in an accuracy of 85% and 86%,respectively.Classification performance is further verified by random sampling using five cross-validation and 30 iterations,which gave an accuracy of 97% and 100% for training and testing.These results demonstrate that the proposed approach can help develop a pre-warning system to alert the drivers approaching signalized intersections,thus reducing back-end crash and accidents.展开更多
In array signal processing,number of signals is often a premise of estimating other parameters.For the sake of determining signal number in the condition of strong additive noise or a little sample data,an algorithm f...In array signal processing,number of signals is often a premise of estimating other parameters.For the sake of determining signal number in the condition of strong additive noise or a little sample data,an algorithm for detecting number of wideband signals is provided.First,technique of focusing is used for transforming signals into a same focusing subspace.Then the support vector machine(SVM)can be deduced by the information of eigenvalues and corresponding eigenvectors.At last,the signal number can be determined with the obtained decision function.Several simulations have been carried on verifying the proposed algorithm.展开更多
In this paper, a classification method based on Support Vector Machine (SVM) is given in the digital modulation signal classification. The second, fourth and sixth order cumulants of the received signals are used as c...In this paper, a classification method based on Support Vector Machine (SVM) is given in the digital modulation signal classification. The second, fourth and sixth order cumulants of the received signals are used as classification vectors firstly, then the kernel thought is used to map the feature vector to the high dimensional feature space and the optimum separating hyperplane is constructed in space to realize signal recognition. In order to build an effective and robust SVM classifier, the radial basis kernel function is selected, one against one or one against rest of multi-class classifier is designed, and method of parameter selection using cross- validation grid is adopted. Through the experiments it can be concluded that the classifier based on SVM has high performance and is more robust.展开更多
To generate high-frequency radio frequency(RF) vector signals, a vector signal generation method by optical frequency sextupling using a dual-parallel modulator is proposed. The method modulates vector signal on +3 rd...To generate high-frequency radio frequency(RF) vector signals, a vector signal generation method by optical frequency sextupling using a dual-parallel modulator is proposed. The method modulates vector signal on +3 rd order optical sideband and local oscillator(LO) signal on-3 rd order sideband using the intermodulation process in the DPMZM. After suppressing of the optical carrier and other sidebands through proper adjustment for modulator biases and modulation index, a frequency sextupled millimeter-wave vector signal can be generated after photodetection. The frequency sextupling will lower the bandwidth of the modulator, the local oscillator and the driving circuits. In addition, the phase of generated signal is not distorted after detection, and the power fading after fiber transmission can be avoided. In the simulation, a 500-MSym/s QPSK signal at 60 GHz is generated by 10-GHz drive signal. After travelling over fiber with length of 20/30/40-km, receiver power penalty keeps below 2.5 dB.展开更多
A novel compression method for mechanical vibrating signals,binding with sub-band vector quantization(SVQ) by wavelet packet transformation(WPT) and discrete cosine transformation(DCT) is proposed.Firstly,the vibratin...A novel compression method for mechanical vibrating signals,binding with sub-band vector quantization(SVQ) by wavelet packet transformation(WPT) and discrete cosine transformation(DCT) is proposed.Firstly,the vibrating signal is decomposed into sub-bands by WPT.Then DCT and adaptive bit allocation are done per sub-band and SVQ is performed in each sub-band.It is noted that,after DCT,we only need to code the first components whose numbers are determined by the bits allocated to that sub-band.Through an actual signal,our algorithm is proven to improve the signal-to-noise ratio(SNR) of the reconstructed signal effectively,especially in the situation of lowrate transmission.展开更多
This paper investigates the effect of the Phase Angle Error of a Constant Amplitude Voltage signal in determining the Total Vector Error (TVE) of the Phasor Measurement Unit (PMU) using MATLAB/Simulink. The phase angl...This paper investigates the effect of the Phase Angle Error of a Constant Amplitude Voltage signal in determining the Total Vector Error (TVE) of the Phasor Measurement Unit (PMU) using MATLAB/Simulink. The phase angle error is measured as a function of time in microseconds at four points on the IEEE 14-bus system. When the 1 pps Global Positioning System (GPS) signal to the PMU is lost, sampling of voltage signals on the power grid is done at different rates as it is a function of time. The relationship between the PMU measured signal phase angle and the sampling rate is established by injecting a constant amplitude signal at two different points on the grid. In the simulation, 64 cycles per second is used as the reference while 24 cycles per second is used to represent the fault condition. Results show that a change in the sampling rate from 64 bps to 24 bps in the PMUs resulted in phase angle error in the voltage signals measured by the PMU at four VI Measurement points. The phase angle error measurement that was determined as a time function was used to determine the TVE. Results show that (TVE) was more than 1% in all the cases.展开更多
Sparse array design has significant implications for improving the accuracy of direction of arrival(DOA)estimation of non-circular(NC)signals.We propose an extended nested array with a filled sensor(ENAFS)based on the...Sparse array design has significant implications for improving the accuracy of direction of arrival(DOA)estimation of non-circular(NC)signals.We propose an extended nested array with a filled sensor(ENAFS)based on the hole-filling strategy.Specifically,we first introduce the improved nested array(INA)and prove its properties.Subsequently,we extend the sum-difference coarray(SDCA)by adding an additional sensor to fill the holes.Thus the larger uniform degrees of freedom(uDOFs)and virtual array aperture(VAA)can be abtained,and the ENAFS is designed.Finally,the simulation results are given to verify the superiority of the proposed ENAFS in terms of DOF,mutual coupling and estimation performance.展开更多
Support vector machines (SVMs) are utilized for emotion recognition in Chinese speech in this paper. Both binary class discrimination and the multi class discrimination are discussed. It proves that the emotional fe...Support vector machines (SVMs) are utilized for emotion recognition in Chinese speech in this paper. Both binary class discrimination and the multi class discrimination are discussed. It proves that the emotional features construct a nonlinear problem in the input space, and SVMs based on nonlinear mapping can solve it more effectively than other linear methods. Multi class classification based on SVMs with a soft decision function is constructed to classify the four emotion situations. Compared with principal component analysis (PCA) method and modified PCA method, SVMs perform the best result in multi class discrimination by using nonlinear kernel mapping.展开更多
A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters i...A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters is selected from a range of parameters of communication signals including instantaneous amplitude, phase, and frequency. And the Newton-Armijo algorithm is utilized to train the proposed algorithm, namely, smooth CHKS smooth support vector machine (SCHKS-SSVM). Compared with the existing algorithms, the proposed algorithm not only solves the non-differentiable problem of the second order objective function, but also reduces the recognition error. It significantly improves the training speed and also saves a large amount of storage space through large-scale sorting problems. The simulation results show that the recognition rate of the algorithm can batch training. Therefore, the proposed algorithm is suitable for solving the problem of high dimension and its recognition can exceed 95% when the signal-to-noise ratio is no less than 10 dB.展开更多
A new method based on phase difference analysis is proposed for the single-channel mixed signal separation of single-channel radar fuze.This method is used to estimate the mixing coefficients of de-noised signals thro...A new method based on phase difference analysis is proposed for the single-channel mixed signal separation of single-channel radar fuze.This method is used to estimate the mixing coefficients of de-noised signals through the cumulants of mixed signals,solve the candidate data set by the mixing coefficients and signal analytical form,and resolve the problem of vector ambiguity by analyzing the phase differences.The signal separation is realized by exchanging data of the solutions.The waveform similarity coefficients are calculated,and the time鈥攆requency distributions of separated signals are analyzed.The results show that the proposed method is effective.展开更多
Hyperspectral images in remote sensing include hundreds of spectral bands that provide valuable information for accurately identify objects.In this paper,a new method of classifying hyperspectral images using spectral...Hyperspectral images in remote sensing include hundreds of spectral bands that provide valuable information for accurately identify objects.In this paper,a new method of classifying hyperspectral images using spectral spatial information has been presented.Here,using the hyperspectral signal subspace identification(HYSIME)method which estimates the signal and noise correlation matrix and selects a subset of eigenvalues for the best representation of the signal subspace in order to minimize the mean square error,subsets from the main sample space have been extracted.After subspace extraction with the help of the HYSIME method,the edge-preserving filtering(EPF),and classification of the hyperspectral subspace using a support vector machine(SVM),results were then merged into the decision-making level using majority rule to create the spectral-spatial classifier.The simulation results showed that the spectral-spatial classifier presented leads to significant improvement in the accuracy and validity of the classification of Indiana,Pavia and Salinas hyperspectral images,such that it can classify these images with 98.79%,98.88% and 97.31% accuracy,respectively.展开更多
Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task cl...Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state classification.With the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various applications.Guided Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of channels.This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be eliminated.This enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset.The(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.展开更多
This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet pa...This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet packet transform (WPT) is introduced to extract time-frequency joint information. Then the multi-class classifier based on the least squares support vector machine (LS-SVM) is constructed and verified in the various motion classification tasks. The results of contrastive experiments show that different motions can be identified with high accuracy by the presented method. Furthermore, compared with other classifiers with different features, the performance indicates the potential of the SVM techniques combined with WPT in motion classification.展开更多
The code tracking loop is a key component for user positioning. The pseudorange information of Bei Dou B1 signals has been fused and changed for vector tracking, so a correlation output model for complex scenarios is ...The code tracking loop is a key component for user positioning. The pseudorange information of Bei Dou B1 signals has been fused and changed for vector tracking, so a correlation output model for complex scenarios is designed to prevent the propagation of error and valuate the signal performance. The relevant software and hardware factors that affect the output are analyzed.A single channel time-division multiplexing(TDM) method for multicorrelation data extraction is proposed. Statistical characteristics of the correlation output data for both vector and scalar structures are evaluated. Simulation results show that correlation outputs for both structures follow normal or Chi-squared distributions in normal conditions, and the Gamma distribution in harsh conditions. It is shown that a tracking model based on the multi-channel fusion hardly changes the probability distribution of the correlation output in the normal case, but it reduces the ranging error of the code loop, and hence the tracking ability of the code loop for weak signals is improved. Furthermore, vector tracking changes the pseudorange characteristics of channels anytime, and affects the mutual correlation outputs of the code loops in the abnormal case. This study provides a basis for the subsequent design of autonomous integrity algorithms for vector tracking.展开更多
Traditional global navigation satellite system(GNSS)terminals for satellite navigation adopt independent channels to track the signals from different satellites, which results in a lack of information interaction betw...Traditional global navigation satellite system(GNSS)terminals for satellite navigation adopt independent channels to track the signals from different satellites, which results in a lack of information interaction between the channels. Inspired by the vector tracking idea, and drawing lessons from the principle that in the position domain the Taylor expanded pseudorange observations can be used for positioning via the least squares method, this paper proposes a novel least squares-based multi-channel parameter joint estimation(MPJE) method in the signal domain, which not only retains the advantages of channel fusion, but also maintains the flexibility and diversity of the localization algorithm. With achieving optimal carrier to noise ratio as the goal, the proposed method obtains the required code loop and carrier loop parameters for signal tracking in the domain of whole channels. Experimental results indicate that this method fully achieves the assistant fusion advantages of frequency lock loop(FLL), phase lock loop(PLL)and delay lock loop(DLL), making good use of the robustness and dynamic properties of the FLL and the measurement accuracy of the DLL, and is helpful for achieving stable and accurate signal tracking under weak signals and high dynamic stress environments.展开更多
This paper investigates performance improvement via the incorporation of the support vector machine(SVM)into the vector tracking loop(VTL)for the Global Positioning System(GPS)in limited satellite visibility.Unlike th...This paper investigates performance improvement via the incorporation of the support vector machine(SVM)into the vector tracking loop(VTL)for the Global Positioning System(GPS)in limited satellite visibility.Unlike the traditional scalar tracking loop(STL),the tracking and navigation modules in the VTL are not independent anymore since the user’s position can be determined by using the information from other satellites and can be predicted on the basis of the states of the user.The method proposed in this paper makes use of the SVM to bridge the GPS signal and prevent the error growth due to signal outage.Similar to the neural network,the SVM is motivated by its ability to approximate an unknown nonlinear input-output mapping through supervised training.The SVM is employed for predicting adequate numerical control oscillator(NCO)inputs,i.e.,providing better prediction of residuals for the Doppler frequency and code phase in order to maintain regular operation of the navigation system.When the navigation processing is in good condition,the SVM is at the training stage,and the output information from the discriminator and navigation filter is adopted as the inputs.Other machine learning(ML)algorithms such as the radial basis function neural network(RBFNN)and the Adaptive Network-Based Fuzzy Inference System(ANFIS)are employed for comparison.Performance evaluation for the SVM assisted architecture as compared to the RBFNNand ANFIS-assisted methods and the un-assisted VTL will be carried out and the performance evaluation during GPS signal outage will be presented.The proposed design is very useful for navigation during the environment of limited satellite visibility to effectively overcome the problem in the environment of GPS outage.展开更多
文摘This paper presents a novel method for radar emitter signal recognition. First, wavelet packet transform （WPT） is introduced to extract features from radar emitter signals. Then, rough set theory is used to select the optimal feature subset with good discriminability from original feature set, and support vector machines （SVMs） are employed to design classifiers. A large number of experimental results show that the proposed method achieves very high recognition rates for 9 radar emitter signals in a wide range of signal-to-noise rates, and proves a feasible and valid method.
基金supported by the National Natural Science Foundation of China (61102106)the Fundamental Research Funds for the Central Universities (HEUCF1208 HEUCF100801)
文摘The existing direction of arrival （DOA） estimation algorithms based on the electromagnetic vector sensors array barely deal with the coexisting of independent and coherent signals. A two-dimensional direction finding method using an L-shape electromagnetic vector sensors array is proposed. According to this method, the DOAs of the independent signals and the coherent signals are estimated separately, so that the array aperture can be exploited sufficiently. Firstly, the DOAs of the independent signals are estimated by the estimation of signal parameters via rotational invariance techniques, and the influence of the co- herent signals can be eliminated by utilizing the property of the coherent signals. Then the data covariance matrix containing the information of the coherent signals only is obtained by exploiting the Toeplitz property of the independent signals, and an improved polarimetric angular smoothing technique is proposed to de-correlate the coherent signals. This new method is more practical in actual signal environment than common DOA estimation algorithms and can expand the array aperture. Simulation results are presented to show the estimating performance of the proposed method.
基金the National Natural Science Foundation of China (Nos. 60772007 and 60672008)China Postdoctoral Sci-ence Foundation (No. 20070410258)
文摘Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. In this paper, a novel training algorithm based on total least squares (TLS) for an LS-SVM is presented and applied to multifunctional sensor signal reconstruction. For three different nonlinearities of a multifunctional sensor model, the reconstruction accuracies of input signals are 0.001 36%, 0.031 84% and 0.504 80%, respectively. The experimental results demonstrate the higher reliability and accuracy of the proposed method for multifunctional sensor signal reconstruction than the original LS-SVM training algorithm, and verify the feasibility and stability of the proposed method.
基金supported by Universiti Brunei Darussalam under the University Bursary ScholarshipUniversiti Brunei Darussalam's Research Grants(Nos,UBD/PNC2/2/RG/1(311)and UBD/RSCH/1.11/FICBF/2018/002)。
文摘When the drivers approaching signalized intersections(onset of yellow signal),the drivers would enter into a zone,where they will be in uncertain mode assessing their capabilities to stop or cross the intersection.Therefore,any improper decision might lead to a right-angle or back-end crash.To avoid a right-angle collision,drivers apply the harsh brakes to stop just before the signalized intersection.But this may lead to a back-end crash when the following driver encounters the former's sudden stopping decision.This situation gets multifaceted when the traffic is heterogeneous,containing various types of vehicles.In order to reduce this issue,this study's primary objective is to identify the driving behaviour at signalized intersections based on the driving features(parameters).The secondary objective is to classify the outcome of driving behaviour(safe stopping and unsafe stopping)at the signalized intersection using a support vector machine(SVM)technique.Turning moments are used to identify the zones and label them accordingly for further classification.The classification of 50 instances is identified for training and testing using a 70%-30% rule resulted in an accuracy of 85% and 86%,respectively.Classification performance is further verified by random sampling using five cross-validation and 30 iterations,which gave an accuracy of 97% and 100% for training and testing.These results demonstrate that the proposed approach can help develop a pre-warning system to alert the drivers approaching signalized intersections,thus reducing back-end crash and accidents.
基金This work was supported by the National Natural Science Foundation of China under Grant 61501176Natural Science Foundation of Heilongjiang Province F2018025+1 种基金University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province UNPYSCT-2016017the postdoctoral scientific research developmental fund of Heilongjiang Province in 2017 LBH-Q17149.
文摘In array signal processing,number of signals is often a premise of estimating other parameters.For the sake of determining signal number in the condition of strong additive noise or a little sample data,an algorithm for detecting number of wideband signals is provided.First,technique of focusing is used for transforming signals into a same focusing subspace.Then the support vector machine(SVM)can be deduced by the information of eigenvalues and corresponding eigenvectors.At last,the signal number can be determined with the obtained decision function.Several simulations have been carried on verifying the proposed algorithm.
文摘In this paper, a classification method based on Support Vector Machine (SVM) is given in the digital modulation signal classification. The second, fourth and sixth order cumulants of the received signals are used as classification vectors firstly, then the kernel thought is used to map the feature vector to the high dimensional feature space and the optimum separating hyperplane is constructed in space to realize signal recognition. In order to build an effective and robust SVM classifier, the radial basis kernel function is selected, one against one or one against rest of multi-class classifier is designed, and method of parameter selection using cross- validation grid is adopted. Through the experiments it can be concluded that the classifier based on SVM has high performance and is more robust.
基金Sponsored by the Programme of Introducing Talents of Discipline to Universities(Grant No.B08038)
文摘To generate high-frequency radio frequency(RF) vector signals, a vector signal generation method by optical frequency sextupling using a dual-parallel modulator is proposed. The method modulates vector signal on +3 rd order optical sideband and local oscillator(LO) signal on-3 rd order sideband using the intermodulation process in the DPMZM. After suppressing of the optical carrier and other sidebands through proper adjustment for modulator biases and modulation index, a frequency sextupled millimeter-wave vector signal can be generated after photodetection. The frequency sextupling will lower the bandwidth of the modulator, the local oscillator and the driving circuits. In addition, the phase of generated signal is not distorted after detection, and the power fading after fiber transmission can be avoided. In the simulation, a 500-MSym/s QPSK signal at 60 GHz is generated by 10-GHz drive signal. After travelling over fiber with length of 20/30/40-km, receiver power penalty keeps below 2.5 dB.
基金Supported by the National Natural Science Foundation of China(No.51135001)
文摘A novel compression method for mechanical vibrating signals,binding with sub-band vector quantization(SVQ) by wavelet packet transformation(WPT) and discrete cosine transformation(DCT) is proposed.Firstly,the vibrating signal is decomposed into sub-bands by WPT.Then DCT and adaptive bit allocation are done per sub-band and SVQ is performed in each sub-band.It is noted that,after DCT,we only need to code the first components whose numbers are determined by the bits allocated to that sub-band.Through an actual signal,our algorithm is proven to improve the signal-to-noise ratio(SNR) of the reconstructed signal effectively,especially in the situation of lowrate transmission.
文摘This paper investigates the effect of the Phase Angle Error of a Constant Amplitude Voltage signal in determining the Total Vector Error (TVE) of the Phasor Measurement Unit (PMU) using MATLAB/Simulink. The phase angle error is measured as a function of time in microseconds at four points on the IEEE 14-bus system. When the 1 pps Global Positioning System (GPS) signal to the PMU is lost, sampling of voltage signals on the power grid is done at different rates as it is a function of time. The relationship between the PMU measured signal phase angle and the sampling rate is established by injecting a constant amplitude signal at two different points on the grid. In the simulation, 64 cycles per second is used as the reference while 24 cycles per second is used to represent the fault condition. Results show that a change in the sampling rate from 64 bps to 24 bps in the PMUs resulted in phase angle error in the voltage signals measured by the PMU at four VI Measurement points. The phase angle error measurement that was determined as a time function was used to determine the TVE. Results show that (TVE) was more than 1% in all the cases.
基金supported by China National Science Foundations(Nos.62371225,62371227)。
文摘Sparse array design has significant implications for improving the accuracy of direction of arrival(DOA)estimation of non-circular(NC)signals.We propose an extended nested array with a filled sensor(ENAFS)based on the hole-filling strategy.Specifically,we first introduce the improved nested array(INA)and prove its properties.Subsequently,we extend the sum-difference coarray(SDCA)by adding an additional sensor to fill the holes.Thus the larger uniform degrees of freedom(uDOFs)and virtual array aperture(VAA)can be abtained,and the ENAFS is designed.Finally,the simulation results are given to verify the superiority of the proposed ENAFS in terms of DOF,mutual coupling and estimation performance.
文摘Support vector machines (SVMs) are utilized for emotion recognition in Chinese speech in this paper. Both binary class discrimination and the multi class discrimination are discussed. It proves that the emotional features construct a nonlinear problem in the input space, and SVMs based on nonlinear mapping can solve it more effectively than other linear methods. Multi class classification based on SVMs with a soft decision function is constructed to classify the four emotion situations. Compared with principal component analysis (PCA) method and modified PCA method, SVMs perform the best result in multi class discrimination by using nonlinear kernel mapping.
基金supported by the National Natural Science Foundation of China(61401196)the Jiangsu Provincial Natural Science Foundation of China(BK20140954)+1 种基金the Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory(KX152600015/ITD-U15006)the Beijing Shengfeifan Electronic System Technology Development Co.,Ltd(KY10800150036)
文摘A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters is selected from a range of parameters of communication signals including instantaneous amplitude, phase, and frequency. And the Newton-Armijo algorithm is utilized to train the proposed algorithm, namely, smooth CHKS smooth support vector machine (SCHKS-SSVM). Compared with the existing algorithms, the proposed algorithm not only solves the non-differentiable problem of the second order objective function, but also reduces the recognition error. It significantly improves the training speed and also saves a large amount of storage space through large-scale sorting problems. The simulation results show that the recognition rate of the algorithm can batch training. Therefore, the proposed algorithm is suitable for solving the problem of high dimension and its recognition can exceed 95% when the signal-to-noise ratio is no less than 10 dB.
文摘A new method based on phase difference analysis is proposed for the single-channel mixed signal separation of single-channel radar fuze.This method is used to estimate the mixing coefficients of de-noised signals through the cumulants of mixed signals,solve the candidate data set by the mixing coefficients and signal analytical form,and resolve the problem of vector ambiguity by analyzing the phase differences.The signal separation is realized by exchanging data of the solutions.The waveform similarity coefficients are calculated,and the time鈥攆requency distributions of separated signals are analyzed.The results show that the proposed method is effective.
文摘Hyperspectral images in remote sensing include hundreds of spectral bands that provide valuable information for accurately identify objects.In this paper,a new method of classifying hyperspectral images using spectral spatial information has been presented.Here,using the hyperspectral signal subspace identification(HYSIME)method which estimates the signal and noise correlation matrix and selects a subset of eigenvalues for the best representation of the signal subspace in order to minimize the mean square error,subsets from the main sample space have been extracted.After subspace extraction with the help of the HYSIME method,the edge-preserving filtering(EPF),and classification of the hyperspectral subspace using a support vector machine(SVM),results were then merged into the decision-making level using majority rule to create the spectral-spatial classifier.The simulation results showed that the spectral-spatial classifier presented leads to significant improvement in the accuracy and validity of the classification of Indiana,Pavia and Salinas hyperspectral images,such that it can classify these images with 98.79%,98.88% and 97.31% accuracy,respectively.
基金Funding for this study is received from Taif University Researchers Supporting Project No.(Project No.TURSP-2020/150)Taif University,Taif,Saudi Arabia。
文摘Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state classification.With the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various applications.Guided Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of channels.This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be eliminated.This enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the dataset.The(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.
基金Supported by the National Basic Research Program("973"Program, No2005CB724303 )
文摘This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet packet transform (WPT) is introduced to extract time-frequency joint information. Then the multi-class classifier based on the least squares support vector machine (LS-SVM) is constructed and verified in the various motion classification tasks. The results of contrastive experiments show that different motions can be identified with high accuracy by the presented method. Furthermore, compared with other classifiers with different features, the performance indicates the potential of the SVM techniques combined with WPT in motion classification.
基金supported by the National Natural Science Fundation of China(41474027)
文摘The code tracking loop is a key component for user positioning. The pseudorange information of Bei Dou B1 signals has been fused and changed for vector tracking, so a correlation output model for complex scenarios is designed to prevent the propagation of error and valuate the signal performance. The relevant software and hardware factors that affect the output are analyzed.A single channel time-division multiplexing(TDM) method for multicorrelation data extraction is proposed. Statistical characteristics of the correlation output data for both vector and scalar structures are evaluated. Simulation results show that correlation outputs for both structures follow normal or Chi-squared distributions in normal conditions, and the Gamma distribution in harsh conditions. It is shown that a tracking model based on the multi-channel fusion hardly changes the probability distribution of the correlation output in the normal case, but it reduces the ranging error of the code loop, and hence the tracking ability of the code loop for weak signals is improved. Furthermore, vector tracking changes the pseudorange characteristics of channels anytime, and affects the mutual correlation outputs of the code loops in the abnormal case. This study provides a basis for the subsequent design of autonomous integrity algorithms for vector tracking.
基金supported by the National Natural Science Foundation of China(41474027)National Defense Basic Science Project of China(JCKY2016110B004)
文摘Traditional global navigation satellite system(GNSS)terminals for satellite navigation adopt independent channels to track the signals from different satellites, which results in a lack of information interaction between the channels. Inspired by the vector tracking idea, and drawing lessons from the principle that in the position domain the Taylor expanded pseudorange observations can be used for positioning via the least squares method, this paper proposes a novel least squares-based multi-channel parameter joint estimation(MPJE) method in the signal domain, which not only retains the advantages of channel fusion, but also maintains the flexibility and diversity of the localization algorithm. With achieving optimal carrier to noise ratio as the goal, the proposed method obtains the required code loop and carrier loop parameters for signal tracking in the domain of whole channels. Experimental results indicate that this method fully achieves the assistant fusion advantages of frequency lock loop(FLL), phase lock loop(PLL)and delay lock loop(DLL), making good use of the robustness and dynamic properties of the FLL and the measurement accuracy of the DLL, and is helpful for achieving stable and accurate signal tracking under weak signals and high dynamic stress environments.
基金This work has been partially supported by the Ministry of Science and Technology,Taiwan(Grant numbers MOST 104-2221-E-019-026-MY3 and MOST 109-2221-E019-010).
文摘This paper investigates performance improvement via the incorporation of the support vector machine(SVM)into the vector tracking loop(VTL)for the Global Positioning System(GPS)in limited satellite visibility.Unlike the traditional scalar tracking loop(STL),the tracking and navigation modules in the VTL are not independent anymore since the user’s position can be determined by using the information from other satellites and can be predicted on the basis of the states of the user.The method proposed in this paper makes use of the SVM to bridge the GPS signal and prevent the error growth due to signal outage.Similar to the neural network,the SVM is motivated by its ability to approximate an unknown nonlinear input-output mapping through supervised training.The SVM is employed for predicting adequate numerical control oscillator(NCO)inputs,i.e.,providing better prediction of residuals for the Doppler frequency and code phase in order to maintain regular operation of the navigation system.When the navigation processing is in good condition,the SVM is at the training stage,and the output information from the discriminator and navigation filter is adopted as the inputs.Other machine learning(ML)algorithms such as the radial basis function neural network(RBFNN)and the Adaptive Network-Based Fuzzy Inference System(ANFIS)are employed for comparison.Performance evaluation for the SVM assisted architecture as compared to the RBFNNand ANFIS-assisted methods and the un-assisted VTL will be carried out and the performance evaluation during GPS signal outage will be presented.The proposed design is very useful for navigation during the environment of limited satellite visibility to effectively overcome the problem in the environment of GPS outage.
