This paper proposes a new method for estimating the parameter of maneuvering targets based on sparse time-frequency transform in over-the-horizon radar（OTHR）. In this method, the sparse time-frequency distribution o...This paper proposes a new method for estimating the parameter of maneuvering targets based on sparse time-frequency transform in over-the-horizon radar（OTHR）. In this method, the sparse time-frequency distribution of the radar echo is obtained by solving a sparse optimization problem based on the short-time Fourier transform. Then Hough transform is employed to estimate the parameter of the targets. The proposed algorithm has the following advantages： Compared with the Wigner-Hough transform method, the computational complexity of the sparse optimization is low due to the application of fast Fourier transform（FFT）. And the computational cost of Hough transform is also greatly reduced because of the sparsity of the time-frequency distribution. Compared with the high order ambiguity function（HAF） method, the proposed method improves in terms of precision and robustness to noise. Simulation results show that compared with the HAF method, the required SNR and relative mean square error are 8 dB lower and 50 dB lower respectively in the proposed method. While processing the field experiment data, the execution time of Hough transform in the proposed method is only 4% of the Wigner-Hough transform method.展开更多
In this paper,we address the problem of multiple frequency-hopping(FH)signal parameters estimation in the presence of random missing observations.A space-time matrix with random missing observations is acquired by a u...In this paper,we address the problem of multiple frequency-hopping(FH)signal parameters estimation in the presence of random missing observations.A space-time matrix with random missing observations is acquired by a uniform linear array(ULA).We exploit the inherent incomplete data processing capability of atomic norm soft thresholding(AST)to analyze the space-time matrix and complete the accurate estimation of the hopping time and frequency of the received FH signals.The hopping time is obtained by the sudden changes of the spatial information,which is implemented as the boundary to divide the time domain signal so that each segment of the signal is a superposition of time-invariant multiple components.Then,the frequency of multiple signal components can be estimated precisely by AST within each segment.After obtaining the above two parameters of the hopping time and the frequency of signals,the direction of arrival(DOA)can be directly calculated by them,and the network sorting can be realized.Results of simulation show that the proposed method is superior to the existing technology.Even when a large portion of data observations is missing,as the number of array elements increases,the proposed method still achieves acceptable accuracy of multi-FH signal parameters estimation.展开更多
Introducing frequency agility into a distributed multipleinput multiple-output(MIMO)radar can significantly enhance its anti-jamming ability.However,it would cause the sidelobe pedestal problem in multi-target paramet...Introducing frequency agility into a distributed multipleinput multiple-output(MIMO)radar can significantly enhance its anti-jamming ability.However,it would cause the sidelobe pedestal problem in multi-target parameter estimation.Sparse recovery is an effective way to address this problem,but it cannot be directly utilized for multi-target parameter estimation in frequency-agile distributed MIMO radars due to spatial diversity.In this paper,we propose an algorithm for multi-target parameter estimation according to the signal model of frequency-agile distributed MIMO radars,by modifying the orthogonal matching pursuit(OMP)algorithm.The effectiveness of the proposed method is then verified by simulation results.展开更多
Conventional parameter estimation methods for pseudo-random binary code-linear frequency modulation(PRBC-LFM)signals require prior knowledge,are computationally complex,and exhibit poor performance at low signal-to-no...Conventional parameter estimation methods for pseudo-random binary code-linear frequency modulation(PRBC-LFM)signals require prior knowledge,are computationally complex,and exhibit poor performance at low signal-to-noise ratios(SNRs).To overcome these problems,a blind parameter estimation method based on a Duffing oscillator array is proposed.A new relationship formula among the state of the Duffing oscillator,the pseudo-random sequence of the PRBC-LFM signal,and the frequency difference between the PRBC-LFM signal and the periodic driving force signal of the Duffing oscillator is derived,providing the theoretical basis for blind parameter estimation.Methods based on amplitude method,short-time Fourier transform method,and power spectrum entropy method are used to binarize the output of the Duffing oscillator array,and their performance is compared.The pseudo-random sequence is estimated using Duffing oscillator array synchronization,and the carrier frequency parameters are obtained by the relational expressions and characteristics of the difference frequency.Simulation results show that this blind estimation method overcomes limitations in prior knowledge and maintains good parameter estimation performance up to an SNR of-35 dB.展开更多
In view of the complexity of existing linear frequency modulation(LFM)signal parameter estimation methods and the poor antinoise performance and estimation accuracy under a low signal-to-noise ratio(SNR),a parameter e...In view of the complexity of existing linear frequency modulation(LFM)signal parameter estimation methods and the poor antinoise performance and estimation accuracy under a low signal-to-noise ratio(SNR),a parameter estimation method for LFM signals with a Duffing oscillator based on frequency periodicity is proposed in this paper.This method utilizes the characteristic that the output signal of the Duffing oscillator excited by the LFM signal changes periodically with frequency,and the modulation period of the LFM signal is estimated by autocorrelation processing of the output signal of the Duffing oscillator.On this basis,the corresponding relationship between the reference frequency of the frequencyaligned Duffing oscillator and the frequency range of the LFM signal is analyzed by the periodic power spectrum method,and the frequency information of the LFM signal is determined.Simulation results show that this method can achieve high-accuracy parameter estimation for LFM signals at an SNR of-25 dB.展开更多
In this paper,parameter estimation of linear frequency modulation(LFM)signals containing additive white Gaussian noise is studied.Because the center frequency estimation of an LFM signal is affected by the error propa...In this paper,parameter estimation of linear frequency modulation(LFM)signals containing additive white Gaussian noise is studied.Because the center frequency estimation of an LFM signal is affected by the error propagation effect,resulting in a higher signal to noise ratio(SNR)threshold,a parameter estimation method for LFM signals based on time reversal is proposed.The proposed method avoids SNR loss in the process of estimating the frequency,thus reducing the SNR threshold.The simulation results show that the threshold is reduced by 5 dB compared with the discrete polynomial transform(DPT)method,and the root-mean-square error(RMSE)of the proposed estimator is close to the Cramer-Rao lower bound(CRLB).展开更多
Sine Non-linear Chirp Keying(SNCK) is a kind of high-efficient modulation scheme, which provides a potential new beamforming method in communication and radar systems. It has been proved to have advantages in some par...Sine Non-linear Chirp Keying(SNCK) is a kind of high-efficient modulation scheme, which provides a potential new beamforming method in communication and radar systems. It has been proved to have advantages in some parameter estimation issues over conventional modulation schemes. In this paper, a novel transform termed as Discrete Sinusoidal Frequency Modulation transform(DSFMT) is proposed. Then, the DSFMT of SNCK signal is deduced and classified into three types, based on which, the time-bandwidth product is estimated by the proposed algorithm. Simulation results show that the noise has a signifi cant impact on the localization of the peak value and the time-bandwidth product can be estimated by using local ratio values when.展开更多
That a model has sensitivity responses to parameter uncertainties is a key concept in implementing model parameter es- timation using filtering theory and methodology. Depending on the nature of associated physics and...That a model has sensitivity responses to parameter uncertainties is a key concept in implementing model parameter es- timation using filtering theory and methodology. Depending on the nature of associated physics and characteristic variability of the fluid in a coupled system, the response time scales of a model to parameters can be different, from hourly to decadal. Unlike state estimation, where the update frequency is usually linked with observational frequency, the update frequency for parameter estimation must be associated with the time scale of the model sensitivity response to the parameter being esti- mated. Here, with a simple coupled model, the impact of model sensitivity response time scales on coupled model parameter estimation is studied. The model includes characteristic synoptic to decadal scales by coupling a long-term varying deep ocean with a slow-varying upper ocean forced by a chaotic atmosphere. Results show that, using the update frequency deter- mined by the model sensitivity response time scale, both the reliability and quality of parameter estimation can be improved significantly, and thus the estimated parameters make the model more consistent with the observation. These simple model results provide a guideline for when real observations are used to optimize the parameters in a coupled general circulation model for improving climate analysis and prediction initialization.展开更多
An LMS-like algorithm is applied for estimating the time-varying parameter theta-n in the linear model y(n) = phi-n-tau-theta-n + upsilon-n, which is general in the sense that none of the probabilistic properties such...An LMS-like algorithm is applied for estimating the time-varying parameter theta-n in the linear model y(n) = phi-n-tau-theta-n + upsilon-n, which is general in the sense that none of the probabilistic properties such as stationarity, Markov property, independence and ergodicity is imposed on any of the processes {y(n)}, {phi-n}, {theta-n} and {upsilon-n}. It is shown that the alpha-th moment of the estimation error is of order of the alpha-th moment of the observation noise and the parameter variation w(n) change in equivalence theta-n - theta-n-1.展开更多
Communication opportunities among vehicles are important for data transmission over the Internet of Vehicles(IoV).Mixture models are appropriate to describe complex spatial-temporal data.By calculating the expectation...Communication opportunities among vehicles are important for data transmission over the Internet of Vehicles(IoV).Mixture models are appropriate to describe complex spatial-temporal data.By calculating the expectation of hidden variables in vehicle communication,Expectation Maximization(EM)algorithm solves the maximum likelihood estimation of parameters,and then obtains the mixture model of vehicle communication opportunities.However,the EM algorithm requires multiple iterations and each iteration needs to process all the data.Thus its computational complexity is high.A parameter estimation algorithm with low computational complexity based on Bin Count(BC)and Differential Evolution(DE)(PEBCDE)is proposed.It overcomes the disadvantages of the EM algorithm in solving mixture models for big data.In order to reduce the computational complexity of the mixture models in the IoV,massive data are divided into relatively few time intervals and then counted.According to these few counted values,the parameters of the mixture model are obtained by using DE algorithm.Through modeling and analysis of simulation data and instance data,the PEBCDE algorithm is verified and discussed from two aspects,i.e.,accuracy and efficiency.The numerical solution of the probability distribution parameters is obtained,which further provides a more detailed statistical model for the distribution of the opportunity interval of the IoV.展开更多
In this paper, the parameter estimation problem is investigated for the continuous time stochastic logistic diffusion system. A new continuous process is built based on the likelihood ratio scheme, the Radon-Nikodym d...In this paper, the parameter estimation problem is investigated for the continuous time stochastic logistic diffusion system. A new continuous process is built based on the likelihood ratio scheme, the Radon-Nikodym derivative and the explicit expressions of the error of estimation are given under this new continuous process. By using the random time transformations, law of large numbers for martingales, law of iterated logarithm and stationary distribution of solution, the consistency property are proved for the estimation error. Finally, a numerical simulation is presented to demonstrate the effectiveness of the proposed method in this paper.展开更多
In recent years,the time-frequency overlapping multi-carrier signal has been a novel and valuable topic in blind signal processing,especially in the non-cooperative receiving field.But there is little related research...In recent years,the time-frequency overlapping multi-carrier signal has been a novel and valuable topic in blind signal processing,especially in the non-cooperative receiving field.But there is little related research in public published papers.This paper proposes two timing estimation algorithms,which are non-data-aided and based on the cyclic auto-correlation function.In order to evaluate the performance of the proposed algorithms,the theoretical bound of the timing estimation is derived.According to the analyses and simulation results,the effectiveness of the proposed algorithms has been demonstrated.It shows that MethodⅠhas better performance than MethodⅡ.However,MethodⅡdoes not need prior information,so it has a wider range of applications.展开更多
This paper deals with the problem of H∞ fault estimation for linear time-delay systems in finite frequency domain.First a generalized coordinate change is applied to the original system such that in the new coordinat...This paper deals with the problem of H∞ fault estimation for linear time-delay systems in finite frequency domain.First a generalized coordinate change is applied to the original system such that in the new coordinates all the time-delay terms are injected by the system＇s input and output.Then an observer-based H∞ fault estimator with input and output injections is proposed for fault estimation with known frequency range.With the aid of Generalized Kalman-Yakubovich-Popov lemma,sufficient conditions on the existence of the H∞ fault estimator are derived and a solution to the observer gain matrices is obtained by solving a set of linear matrix inequalities.Finally,a numerical example is given to illustrate the effectiveness of the proposed method.展开更多
By using the sparsity of frequency hopping（FH） signals,an underdetermined blind source separation（UBSS） algorithm is presented. Firstly, the short time Fourier transform（STFT） is performed on the mixed signals. ...By using the sparsity of frequency hopping（FH） signals,an underdetermined blind source separation（UBSS） algorithm is presented. Firstly, the short time Fourier transform（STFT） is performed on the mixed signals. Then, the mixing matrix, hopping frequencies, hopping instants and the hooping rate can be estimated by the K-means clustering algorithm. With the estimated mixing matrix, the directions of arrival（DOA） of source signals can be obtained. Then, the FH signals are sorted and the FH pattern is obtained. Finally, the shortest path algorithm is adopted to recover the time domain signals. Simulation results show that the correlation coefficient between the estimated FH signal and the source signal is above 0.9 when the signal-to-noise ratio（SNR） is higher than 0 d B and hopping parameters of multiple FH signals in the synchronous orthogonal FH network can be accurately estimated and sorted under the underdetermined conditions.展开更多
Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Dopple...Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio(SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator(RBFSD) based on the pseudo-noise(PN) sequence.The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than -15dB. The proposed algorithm is about 1/D times less complex than the existing PN pilot sequence algorithm, where D is the resolution of the fractional Doppler.展开更多
A novel algorithm based on Radon-Ambiguity Transform (RAT) and Adaptive Signal Decomposition (ASD) is presented for the detection and parameter estimation of multicompo-nent Linear Frequency Modulated (LFM) signals. T...A novel algorithm based on Radon-Ambiguity Transform (RAT) and Adaptive Signal Decomposition (ASD) is presented for the detection and parameter estimation of multicompo-nent Linear Frequency Modulated (LFM) signals. The key problem lies in the chirplet estimation. Genetic algorithm is employed to search for the optimization parameter of chirplet. High estimation accuracy can be obtained even at low Signal-to-Noisc Ratio(SNR). Finally simulation results are provided to demonstrate the performance of the proposed algorithm.展开更多
Blade-health monitoring is intensely required for turbomachinery because of the high failure risk of rotating blades.Blade-Tip Timing(BTT)is considered as the most promising technique for operational blade-vibration m...Blade-health monitoring is intensely required for turbomachinery because of the high failure risk of rotating blades.Blade-Tip Timing(BTT)is considered as the most promising technique for operational blade-vibration monitoring,which obtains the parameters that characterize the blade condition from recorded signals.However,its application is hindered by severe undersampling and stringent probe layouts.An inappropriate probe layout can make most of the existing methods invalid or inaccurate.Additionally,a general conflict arises between the allowed and required layouts because of arrangement restrictions.For the sake of economy and safety,parameter identification based on fewer probes has been preferred by users.In this work,a spatial-transformation-based method for parameter identification is proposed based on a single-probe BTT measurement.To present the general Sampling-Aliasing Frequency(SAFE)map definition,the traditional time-frequency analysis methods are extended to a time-sampling frequency.Then,a SAFE map is projected onto a parameter space using spatial transformation to extract the slope and intercept parameters,which can be physically interpreted as an engine order and a natural frequency using coordinate transformation.Finally,the effectiveness and robustness of the proposed method are verified by simulations and experiments under uniformly and nonuniformly variable speed conditions.展开更多
The traditional fractional frequency offset(FFO) estimation schemes for orthogonal frequency division multiplexing(OFDM) in non-cooperative communication have the problems of susceptible performance with the frequency...The traditional fractional frequency offset(FFO) estimation schemes for orthogonal frequency division multiplexing(OFDM) in non-cooperative communication have the problems of susceptible performance with the frequency offset values and the number of OFDM symbols,a novel fractional frequency offset blind estimation scheme based on EKF for OFDM systems is conceived.The nonlinear function of the frequency offset is calculated by employing the correlation.And then the frequency offset is estimated by means of the iterative algorithm of EKF.The finally fractional frequency offset is estimated by adopting repeated the above process.Simulation results demonstrate that the proposed scheme is robust to the frequency offset values without any requirements of a prior knowledge.展开更多
The influence on seismic hazard estimation for 310 cities and towns in the whole nation are studied in particular,owing to uncertainty of seismicity parameters caused by non-uniqueness in selecting statistical time ra...The influence on seismic hazard estimation for 310 cities and towns in the whole nation are studied in particular,owing to uncertainty of seismicity parameters caused by non-uniqueness in selecting statistical time ranges. Andthe regional sketch maps of the average varying values of intensity and the average relative varying values of peakacceleration with different probability of exceedance in 50 years are drawn in the Chinese mainland.展开更多
基金supported by the National Natural Science Foundation of China(611011726137118461301262)
文摘This paper proposes a new method for estimating the parameter of maneuvering targets based on sparse time-frequency transform in over-the-horizon radar（OTHR）. In this method, the sparse time-frequency distribution of the radar echo is obtained by solving a sparse optimization problem based on the short-time Fourier transform. Then Hough transform is employed to estimate the parameter of the targets. The proposed algorithm has the following advantages： Compared with the Wigner-Hough transform method, the computational complexity of the sparse optimization is low due to the application of fast Fourier transform（FFT）. And the computational cost of Hough transform is also greatly reduced because of the sparsity of the time-frequency distribution. Compared with the high order ambiguity function（HAF） method, the proposed method improves in terms of precision and robustness to noise. Simulation results show that compared with the HAF method, the required SNR and relative mean square error are 8 dB lower and 50 dB lower respectively in the proposed method. While processing the field experiment data, the execution time of Hough transform in the proposed method is only 4% of the Wigner-Hough transform method.
文摘In this paper,we address the problem of multiple frequency-hopping(FH)signal parameters estimation in the presence of random missing observations.A space-time matrix with random missing observations is acquired by a uniform linear array(ULA).We exploit the inherent incomplete data processing capability of atomic norm soft thresholding(AST)to analyze the space-time matrix and complete the accurate estimation of the hopping time and frequency of the received FH signals.The hopping time is obtained by the sudden changes of the spatial information,which is implemented as the boundary to divide the time domain signal so that each segment of the signal is a superposition of time-invariant multiple components.Then,the frequency of multiple signal components can be estimated precisely by AST within each segment.After obtaining the above two parameters of the hopping time and the frequency of signals,the direction of arrival(DOA)can be directly calculated by them,and the network sorting can be realized.Results of simulation show that the proposed method is superior to the existing technology.Even when a large portion of data observations is missing,as the number of array elements increases,the proposed method still achieves acceptable accuracy of multi-FH signal parameters estimation.
文摘Introducing frequency agility into a distributed multipleinput multiple-output(MIMO)radar can significantly enhance its anti-jamming ability.However,it would cause the sidelobe pedestal problem in multi-target parameter estimation.Sparse recovery is an effective way to address this problem,but it cannot be directly utilized for multi-target parameter estimation in frequency-agile distributed MIMO radars due to spatial diversity.In this paper,we propose an algorithm for multi-target parameter estimation according to the signal model of frequency-agile distributed MIMO radars,by modifying the orthogonal matching pursuit(OMP)algorithm.The effectiveness of the proposed method is then verified by simulation results.
基金the National Natural Science Foundation of China(Grant Nos.61973037 and 61673066).
文摘Conventional parameter estimation methods for pseudo-random binary code-linear frequency modulation(PRBC-LFM)signals require prior knowledge,are computationally complex,and exhibit poor performance at low signal-to-noise ratios(SNRs).To overcome these problems,a blind parameter estimation method based on a Duffing oscillator array is proposed.A new relationship formula among the state of the Duffing oscillator,the pseudo-random sequence of the PRBC-LFM signal,and the frequency difference between the PRBC-LFM signal and the periodic driving force signal of the Duffing oscillator is derived,providing the theoretical basis for blind parameter estimation.Methods based on amplitude method,short-time Fourier transform method,and power spectrum entropy method are used to binarize the output of the Duffing oscillator array,and their performance is compared.The pseudo-random sequence is estimated using Duffing oscillator array synchronization,and the carrier frequency parameters are obtained by the relational expressions and characteristics of the difference frequency.Simulation results show that this blind estimation method overcomes limitations in prior knowledge and maintains good parameter estimation performance up to an SNR of-35 dB.
基金Project supported by the National Natural Science Foundation of China(Grant No.61973037)。
文摘In view of the complexity of existing linear frequency modulation(LFM)signal parameter estimation methods and the poor antinoise performance and estimation accuracy under a low signal-to-noise ratio(SNR),a parameter estimation method for LFM signals with a Duffing oscillator based on frequency periodicity is proposed in this paper.This method utilizes the characteristic that the output signal of the Duffing oscillator excited by the LFM signal changes periodically with frequency,and the modulation period of the LFM signal is estimated by autocorrelation processing of the output signal of the Duffing oscillator.On this basis,the corresponding relationship between the reference frequency of the frequencyaligned Duffing oscillator and the frequency range of the LFM signal is analyzed by the periodic power spectrum method,and the frequency information of the LFM signal is determined.Simulation results show that this method can achieve high-accuracy parameter estimation for LFM signals at an SNR of-25 dB.
基金supported by the Regional Joint Fund for Basic and Applied Basic Research of Guangdong Province(2019B1515120009)the Defense Basic Scientific Research Program(61424132005).
文摘In this paper,parameter estimation of linear frequency modulation(LFM)signals containing additive white Gaussian noise is studied.Because the center frequency estimation of an LFM signal is affected by the error propagation effect,resulting in a higher signal to noise ratio(SNR)threshold,a parameter estimation method for LFM signals based on time reversal is proposed.The proposed method avoids SNR loss in the process of estimating the frequency,thus reducing the SNR threshold.The simulation results show that the threshold is reduced by 5 dB compared with the discrete polynomial transform(DPT)method,and the root-mean-square error(RMSE)of the proposed estimator is close to the Cramer-Rao lower bound(CRLB).
基金supported by Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory(KX152600015/ITD-U15006)National Natural Science Foundation of China(No.61401196)
文摘Sine Non-linear Chirp Keying(SNCK) is a kind of high-efficient modulation scheme, which provides a potential new beamforming method in communication and radar systems. It has been proved to have advantages in some parameter estimation issues over conventional modulation schemes. In this paper, a novel transform termed as Discrete Sinusoidal Frequency Modulation transform(DSFMT) is proposed. Then, the DSFMT of SNCK signal is deduced and classified into three types, based on which, the time-bandwidth product is estimated by the proposed algorithm. Simulation results show that the noise has a signifi cant impact on the localization of the peak value and the time-bandwidth product can be estimated by using local ratio values when.
基金funded by the National Natural Science Foundation of China (Grant No.41676088)the National Key Research and Development Project of China (2016YFC1401800,2017YFC1404100,2017YFC1404102)+1 种基金the Fundamental Research Funds for the Central Universities (HEUCF 041705)the Foundation of the Key Laboratory of Marine Environmental Information Technology
文摘That a model has sensitivity responses to parameter uncertainties is a key concept in implementing model parameter es- timation using filtering theory and methodology. Depending on the nature of associated physics and characteristic variability of the fluid in a coupled system, the response time scales of a model to parameters can be different, from hourly to decadal. Unlike state estimation, where the update frequency is usually linked with observational frequency, the update frequency for parameter estimation must be associated with the time scale of the model sensitivity response to the parameter being esti- mated. Here, with a simple coupled model, the impact of model sensitivity response time scales on coupled model parameter estimation is studied. The model includes characteristic synoptic to decadal scales by coupling a long-term varying deep ocean with a slow-varying upper ocean forced by a chaotic atmosphere. Results show that, using the update frequency deter- mined by the model sensitivity response time scale, both the reliability and quality of parameter estimation can be improved significantly, and thus the estimated parameters make the model more consistent with the observation. These simple model results provide a guideline for when real observations are used to optimize the parameters in a coupled general circulation model for improving climate analysis and prediction initialization.
文摘An LMS-like algorithm is applied for estimating the time-varying parameter theta-n in the linear model y(n) = phi-n-tau-theta-n + upsilon-n, which is general in the sense that none of the probabilistic properties such as stationarity, Markov property, independence and ergodicity is imposed on any of the processes {y(n)}, {phi-n}, {theta-n} and {upsilon-n}. It is shown that the alpha-th moment of the estimation error is of order of the alpha-th moment of the observation noise and the parameter variation w(n) change in equivalence theta-n - theta-n-1.
基金This work was supported by the Fundamental Research Funds for the Central Universities(Grant No.FRF-BD-20-11A)the Scientific and Technological Innovation Foundation of Shunde Graduate School,USTB(Grant No.BK19AF005).
文摘Communication opportunities among vehicles are important for data transmission over the Internet of Vehicles(IoV).Mixture models are appropriate to describe complex spatial-temporal data.By calculating the expectation of hidden variables in vehicle communication,Expectation Maximization(EM)algorithm solves the maximum likelihood estimation of parameters,and then obtains the mixture model of vehicle communication opportunities.However,the EM algorithm requires multiple iterations and each iteration needs to process all the data.Thus its computational complexity is high.A parameter estimation algorithm with low computational complexity based on Bin Count(BC)and Differential Evolution(DE)(PEBCDE)is proposed.It overcomes the disadvantages of the EM algorithm in solving mixture models for big data.In order to reduce the computational complexity of the mixture models in the IoV,massive data are divided into relatively few time intervals and then counted.According to these few counted values,the parameters of the mixture model are obtained by using DE algorithm.Through modeling and analysis of simulation data and instance data,the PEBCDE algorithm is verified and discussed from two aspects,i.e.,accuracy and efficiency.The numerical solution of the probability distribution parameters is obtained,which further provides a more detailed statistical model for the distribution of the opportunity interval of the IoV.
文摘In this paper, the parameter estimation problem is investigated for the continuous time stochastic logistic diffusion system. A new continuous process is built based on the likelihood ratio scheme, the Radon-Nikodym derivative and the explicit expressions of the error of estimation are given under this new continuous process. By using the random time transformations, law of large numbers for martingales, law of iterated logarithm and stationary distribution of solution, the consistency property are proved for the estimation error. Finally, a numerical simulation is presented to demonstrate the effectiveness of the proposed method in this paper.
基金supported by the National Natural Science Foundation of China under Grant No. 61501084。
文摘In recent years,the time-frequency overlapping multi-carrier signal has been a novel and valuable topic in blind signal processing,especially in the non-cooperative receiving field.But there is little related research in public published papers.This paper proposes two timing estimation algorithms,which are non-data-aided and based on the cyclic auto-correlation function.In order to evaluate the performance of the proposed algorithms,the theoretical bound of the timing estimation is derived.According to the analyses and simulation results,the effectiveness of the proposed algorithms has been demonstrated.It shows that MethodⅠhas better performance than MethodⅡ.However,MethodⅡdoes not need prior information,so it has a wider range of applications.
基金supported in part by the National Natural Science Foundation of China (60774071)the National High Technology Research and Development Program of China (863 Program) (2008AA121302)+1 种基金the Major State Basic Research Development Program of China (973 Program) (2009CB724000)the State Scholarship Fund of China
文摘This paper deals with the problem of H∞ fault estimation for linear time-delay systems in finite frequency domain.First a generalized coordinate change is applied to the original system such that in the new coordinates all the time-delay terms are injected by the system＇s input and output.Then an observer-based H∞ fault estimator with input and output injections is proposed for fault estimation with known frequency range.With the aid of Generalized Kalman-Yakubovich-Popov lemma,sufficient conditions on the existence of the H∞ fault estimator are derived and a solution to the observer gain matrices is obtained by solving a set of linear matrix inequalities.Finally,a numerical example is given to illustrate the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(6120113461201135)+2 种基金the 111 Project(B08038)the Fundamental Research Funds for the Central Universities(72124669)the Open Research Fund of the Academy of Application(2014CXJJ-TX06)
文摘By using the sparsity of frequency hopping（FH） signals,an underdetermined blind source separation（UBSS） algorithm is presented. Firstly, the short time Fourier transform（STFT） is performed on the mixed signals. Then, the mixing matrix, hopping frequencies, hopping instants and the hooping rate can be estimated by the K-means clustering algorithm. With the estimated mixing matrix, the directions of arrival（DOA） of source signals can be obtained. Then, the FH signals are sorted and the FH pattern is obtained. Finally, the shortest path algorithm is adopted to recover the time domain signals. Simulation results show that the correlation coefficient between the estimated FH signal and the source signal is above 0.9 when the signal-to-noise ratio（SNR） is higher than 0 d B and hopping parameters of multiple FH signals in the synchronous orthogonal FH network can be accurately estimated and sorted under the underdetermined conditions.
文摘Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio(SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator(RBFSD) based on the pseudo-noise(PN) sequence.The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than -15dB. The proposed algorithm is about 1/D times less complex than the existing PN pilot sequence algorithm, where D is the resolution of the fractional Doppler.
文摘A novel algorithm based on Radon-Ambiguity Transform (RAT) and Adaptive Signal Decomposition (ASD) is presented for the detection and parameter estimation of multicompo-nent Linear Frequency Modulated (LFM) signals. The key problem lies in the chirplet estimation. Genetic algorithm is employed to search for the optimization parameter of chirplet. High estimation accuracy can be obtained even at low Signal-to-Noisc Ratio(SNR). Finally simulation results are provided to demonstrate the performance of the proposed algorithm.
基金supported by the National Key Research and Development Program of China(No.2020YFB2010800)the National Natural Science Foundation of China(Nos.51875433 and 92060302)+1 种基金the Natural Science Foundation of Shaanxi Province,China(No.2019KJXX-043,2021JC-04)the Fundamental Research Funds for the Central Universities and the Foundation of Beilin District,China(No.GX2029)。
文摘Blade-health monitoring is intensely required for turbomachinery because of the high failure risk of rotating blades.Blade-Tip Timing(BTT)is considered as the most promising technique for operational blade-vibration monitoring,which obtains the parameters that characterize the blade condition from recorded signals.However,its application is hindered by severe undersampling and stringent probe layouts.An inappropriate probe layout can make most of the existing methods invalid or inaccurate.Additionally,a general conflict arises between the allowed and required layouts because of arrangement restrictions.For the sake of economy and safety,parameter identification based on fewer probes has been preferred by users.In this work,a spatial-transformation-based method for parameter identification is proposed based on a single-probe BTT measurement.To present the general Sampling-Aliasing Frequency(SAFE)map definition,the traditional time-frequency analysis methods are extended to a time-sampling frequency.Then,a SAFE map is projected onto a parameter space using spatial transformation to extract the slope and intercept parameters,which can be physically interpreted as an engine order and a natural frequency using coordinate transformation.Finally,the effectiveness and robustness of the proposed method are verified by simulations and experiments under uniformly and nonuniformly variable speed conditions.
基金supported by the National Natural Science Foundation of China under Grant No.61501348 and 61271299China Postdoctoral Science Foundation funded project under Grant No.2014M562372+1 种基金Natural Science Basic Research Plan in Shaanxi Province of China under Grant No.2016JQ6039the 111 Project under Grant No.B08038
文摘The traditional fractional frequency offset(FFO) estimation schemes for orthogonal frequency division multiplexing(OFDM) in non-cooperative communication have the problems of susceptible performance with the frequency offset values and the number of OFDM symbols,a novel fractional frequency offset blind estimation scheme based on EKF for OFDM systems is conceived.The nonlinear function of the frequency offset is calculated by employing the correlation.And then the frequency offset is estimated by means of the iterative algorithm of EKF.The finally fractional frequency offset is estimated by adopting repeated the above process.Simulation results demonstrate that the proposed scheme is robust to the frequency offset values without any requirements of a prior knowledge.
文摘The influence on seismic hazard estimation for 310 cities and towns in the whole nation are studied in particular,owing to uncertainty of seismicity parameters caused by non-uniqueness in selecting statistical time ranges. Andthe regional sketch maps of the average varying values of intensity and the average relative varying values of peakacceleration with different probability of exceedance in 50 years are drawn in the Chinese mainland.
