Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates
暂无分享,去创建一个
[1] W.-P. Zhu,et al. Identification of autoregressive moving average systems from noise-corrupted observations , 2008, 2008 Joint 6th International IEEE Northeast Workshop on Circuits and Systems and TAISA Conference.
[2] Wei Xing Zheng. On Estimation of Autoregressive Signals in the Presence of Noise , 2006, IEEE Transactions on Circuits and Systems II: Express Briefs.
[3] Wei Xing Zheng. Adaptive parameter estimation of autoregressive signals from noisy observations , 1998, ICSP '98. 1998 Fourth International Conference on Signal Processing (Cat. No.98TH8344).
[4] Lijuan Jia,et al. On convergence of a BCLS algorithm for noisy autoregressive process estimation , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..
[5] Eric Grivel,et al. Multichannel AR parameter estimation from noisy observations as an errors-in-variables issue , 2008, 2008 16th European Signal Processing Conference.
[6] Tülay Adali,et al. Unbiased RLS identification of errors-in-variables models in the presence of correlated noise , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).
[7] J. Makhoul,et al. Linear prediction: A tutorial review , 1975, Proceedings of the IEEE.
[8] Petar M. Djuric,et al. Sequential Estimation of Hidden ARMA Processes by Particle Filtering—Part II , 2017, IEEE Transactions on Signal Processing.
[9] Yingting Luo,et al. An Advanced Parameter Estimator of Multichannel Autoregressive Signals from Noisy Observations , 2009, 2009 International Conference on Information Engineering and Computer Science.
[10] Stefan Werner,et al. Recursive Total Least-Squares Algorithm Based on Inverse Power Method and Dichotomous Coordinate-Descent Iterations , 2014, IEEE Transactions on Signal Processing.
[11] Wei Xing Zheng. On unbiased parameter estimation of autoregressive signals observed in noise , 2003, Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03..
[12] Carlos E. Davila,et al. On the noise-compensated Yule-Walker equations , 2001, IEEE Trans. Signal Process..
[13] Petros G. Voulgaris,et al. On optimal ℓ∞ to ℓ∞ filtering , 1995, Autom..
[14] K. Hasan,et al. Multichannel autoregressive spectral estimation from noisy observations , 2000, 2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119).
[15] Mohamed Najim,et al. Consistent estimation of autoregressive parameters from noisy observations based on two interacting Kalman filters , 2006, Signal Process..
[16] J. Cadzow. Maximum Entropy Spectral Analysis , 2006 .
[17] J. Cadzow,et al. High performance spectral estimation--A new ARMA method , 1980 .
[18] Petar M. Djuric,et al. Sequential Estimation of Hidden ARMA Processes by Particle Filtering—Part I , 2017, IEEE Transactions on Signal Processing.
[19] Ai-Guo Wu,et al. Bias compensation-based recursive least-squares estimation with forgetting factors for output error moving average systems , 2014, IET Signal Process..
[20] Mohamed A. Deriche. AR parameter estimation from noisy data using the EM algorithm , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.
[21] T. Moon,et al. Mathematical Methods and Algorithms for Signal Processing , 1999 .
[22] James Melsa,et al. Least squares estimation of predictor coefficients from noisy observations , 1977, 1977 IEEE Conference on Decision and Control including the 16th Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications.
[23] Jitendra K. Tugnait. Recursive parameter estimation for noisy autoregressive signals , 1986, IEEE Trans. Inf. Theory.
[24] R. Guidorzi,et al. Identification of ARX models with noisy input and output , 2007, 2007 European Control Conference (ECC).
[25] L. Scharf,et al. A Prony method for noisy data: Choosing the signal components and selecting the order in exponential signal models , 1984, Proceedings of the IEEE.
[26] T. Ulrych,et al. Time series modeling and maximum entropy , 1976 .
[27] S. Kay. Fundamentals of statistical signal processing: estimation theory , 1993 .
[28] T. Sen Lee. Identification and spectral estimation of noisy multivariate autoregressive processes , 1981, ICASSP.
[29] Albert H Nuttall,et al. Spectral Analysis of a Univariate Process with Bad Data Points, via Maximum Entropy and Linear Predictive Techniques , 1976 .
[30] Donald F. Gingras. Estimation of the autoregressive parameters from observations of a noise corrupted autoregressive time series , 1982, ICASSP.
[31] Hing Cheung So. LMS algorithm for unbiased parameter estimation of noisy autoregressive signals , 2001 .
[32] Luca Martino,et al. Distributed Particle Metropolis-Hastings Schemes , 2018, 2018 IEEE Statistical Signal Processing Workshop (SSP).
[33] Wei Xing Zheng. On TLS estimation of autoregressive signals with noisy measurements , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..
[34] Jonathan D. Cryer,et al. Time Series Analysis , 1986 .
[35] Georgios B. Giannakis,et al. Noisy input/output system identification using cumulants and the Steiglitz-McBride algorithm , 1996, IEEE Trans. Signal Process..
[36] Yong Zhang,et al. A modified bias compensation method for output error systems with colored noises , 2011, Proceedings of the 30th Chinese Control Conference.
[37] Gerhard Doblinger,et al. An adaptive Kalman filter for the enhancement of noisy AR signals , 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).
[38] Roberto Guidorzi,et al. A recursive errors-in-variables method for tracking time varying autoregressive parameters from noisy observations , 2010, 2010 18th European Signal Processing Conference.
[39] Inan Güler,et al. AR spectral analysis of EEG signals by using maximum likelihood estimation , 2001, Comput. Biol. Medicine.
[40] Wei Xing Zheng. An Efficient Method for Estimation of Autoregressive Signals Subject to Colored Noise , 2007, 2007 IEEE International Symposium on Circuits and Systems.
[41] A. Wrench,et al. A new approach to noise-robust LPC , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.
[42] M.K. Hasan,et al. A joint technique for autoregressive spectral estimation from noisy observations , 2000, 2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119).
[43] Wei Xing Zheng. Adaptive linear prediction of autoregressive models in the presence of noise , 2000, WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.
[44] Sabine Van Huffel,et al. An efficient and reliable algorithm for computing the singular subspace of a matrix, associated with its smallest singular values , 1987 .
[45] W. X. Zheng. Identification of Autoregressive Signals Observed in Noise , 1993, 1993 American Control Conference.
[46] M. S. Ahmed. Estimating the parameters of a noisy AR-process by using a bootstrap estimator , 1982, ICASSP.
[47] Mohamed Najim,et al. Estimation of autoregressive fading channels based on two cross-coupled H∞ filters , 2009, Signal Image Video Process..
[48] Md. Kamrul Hasan,et al. A new method for parameter estimation of autoregressive signals in colored noise , 2002, 2002 11th European Signal Processing Conference.
[49] R. Lacoss. DATA ADAPTIVE SPECTRAL ANALYSIS METHODS , 1971 .
[50] Karthikeyan Natesan Ramamurthy,et al. MATLAB Software for the Code Excited Linear Prediction Algorithm: The Federal Standard-1016 , 2010, MATLAB Software for the Code Excited Linear Prediction Algorithm.
[51] E. Masry,et al. Autoregressive spectral estimation in additive noise , 1988, IEEE Trans. Acoust. Speech Signal Process..
[52] Yoram Bresler,et al. Exact maximum likelihood parameter estimation of superimposed exponential signals in noise , 1986, IEEE Trans. Acoust. Speech Signal Process..
[53] Ramdas Kumaresan,et al. An algorithm for pole-zero modeling and spectral analysis , 1986, IEEE Trans. Acoust. Speech Signal Process..
[54] L. Mcbride,et al. A technique for the identification of linear systems , 1965 .
[55] Wei Xing Zheng. An efficient method for estimation of autoregressive signals in noise , 2005, 2005 IEEE International Symposium on Circuits and Systems.
[56] Elias Masry,et al. Almost sure convergence analysis of autoregressive spectral estimation in additive noise , 1991, IEEE Trans. Inf. Theory.
[57] Georgios B. Giannakis,et al. Robust RLS in the Presence of Correlated Noise Using Outlier Sparsity , 2012, IEEE Transactions on Signal Processing.
[58] Wei Xing Zheng. Unbiased identification of autoregressive signals observed in colored noise , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).
[59] Wei Xing Zheng. An efficient algorithm for parameter estimation of noisy AR processes , 1997, Proceedings of 1997 IEEE International Symposium on Circuits and Systems. Circuits and Systems in the Information Age ISCAS '97.
[60] Hsiao-Chuan Wang,et al. An extended Levinson-Durbin algorithm for the analysis of noisy autoregressive process , 1996, IEEE Signal Processing Letters.
[61] Steven Kay. Recursive maximum likelihood estimation of autoregressive processes , 1983 .
[62] Takashi Yahagi,et al. Accurate noise compensation technique for the identification of multichannel AR processes with noise , 1996, 1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96.
[63] Roberto Guidorzi,et al. Recursive errors-in-variables approach for ar parameter estimation from noisy observations. Application to radar sea clutter rejection , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.
[64] Steven Kay,et al. The effects of noise on the autoregressive spectral estimator , 1979 .
[65] Thomas L. Marzetta,et al. An IQML type algorithm for AR parameter estimation from noisy covariance sequences , 2009, 2009 17th European Signal Processing Conference.
[66] Wei Xing Zheng. On implementation of a least-squares based algorithm for noisy autoregressive signals , 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).
[67] Tülay Adali,et al. Unbiased Recursive Least-Squares Estimation Utilizing Dichotomous Coordinate-Descent Iterations , 2014, IEEE Transactions on Signal Processing.
[68] A.V. Oppenheim,et al. Enhancement and bandwidth compression of noisy speech , 1979, Proceedings of the IEEE.
[69] L. Marple. A new autoregressive spectrum analysis algorithm , 1980 .
[70] W.-P. Zhu,et al. A correlation domain algorithm for autoregressive system identification from noisy observations , 2008, 2008 51st Midwest Symposium on Circuits and Systems.
[71] Kareem E. Baddour,et al. Autoregressive modeling for fading channel simulation , 2005, IEEE Transactions on Wireless Communications.
[72] Ramdas Kumaresan,et al. Accurate parameter estimation of noisy speech-like signals , 1982, ICASSP.
[73] Wei Xing Zheng. Estimation of autoregressive signals from noisy measurements , 1997 .
[74] Wei Xing Zheng. A new estimation algorithm for AR signals measured in noise , 2002, 6th International Conference on Signal Processing, 2002..
[75] J. Makhoul. Spectral analysis of speech by linear prediction , 1973 .
[76] R. Kumaresan,et al. Improved spectral resolution III: Efficient realization , 1980, Proceedings of the IEEE.
[77] Wei Xing Zheng. A new look at parameter estimation of autoregressive signals from noisy observations , 2006, 2006 IEEE International Symposium on Circuits and Systems.
[78] Sabine Van Huffel,et al. The partial total least squares algorithm , 1988 .
[79] W. Zheng. A least-squares based method for autoregressive signals in the presence of noise , 1999 .
[80] Wei Xing Zheng. An alternative method for noisy autoregressive signal estimation , 2002, 2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353).
[81] Alimorad Mahmoudi,et al. Parameter estimation of noisy autoregressive signals , 2010, 2010 18th Iranian Conference on Electrical Engineering.
[82] Alexander Kukush,et al. Measurement Error Models , 2011, International Encyclopedia of Statistical Science.
[83] John H. L. Hansen,et al. Discrete-Time Processing of Speech Signals , 1993 .
[84] S. Fattah,et al. Identification of noisy AR systems using damped sinusoidal model of autocorrelation function , 2003, IEEE Signal Processing Letters.
[85] Ali Jamoos,et al. Dual optimal filters for parameter estimation of a multivariate autoregressive process from noisy observations , 2011 .
[86] R. Kumaresan,et al. Estimation of frequencies of multiple sinusoids: Making linear prediction perform like maximum likelihood , 1982, Proceedings of the IEEE.
[87] Joos Vandewalle,et al. The Use of Total Linear Least Squares Techniques for Identification and Parameter Estimation , 1985 .
[88] Luis Weruaga,et al. Frequency-selective autoregressive estimation in noise , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.
[89] A. Evans,et al. Optimal least squares time-domain synthesis of recursive digital filters , 1973 .
[90] Wei Xing Zheng. Autoregressive parameter estimation from noisy data , 2000 .
[91] S. Lawrence Marple,et al. High resolution autoregressive spectrum analysis using noise power cancellation , 1978, ICASSP.
[92] Wei Xing Zheng. A fast convergent algorithm for identification of noisy autoregressive signals , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).
[93] Bruno Cernuschi-Frías,et al. On the exact maximum likelihood estimation of Gaussian autoregressive processes , 1988, IEEE Trans. Acoust. Speech Signal Process..
[94] Luis Weruaga. Two methods for autoregressive estimationin noise , 2011, 2011 IEEE GCC Conference and Exhibition (GCC).
[95] Steven Kay,et al. Modern Spectral Estimation: Theory and Application , 1988 .
[96] Wu Ai-guo,et al. Bias compensation based recursive least squares identification for equation error models with colored noises , 2014, Proceedings of the 33rd Chinese Control Conference.
[97] CoranderJukka,et al. Adaptive importance sampling in signal processing , 2015 .
[98] Ramdas Kumaresan,et al. Improved spectral resolution II , 1980, ICASSP.
[99] Alan V. Oppenheim,et al. All-pole modeling of degraded speech , 1978 .
[100] Eric Grivel,et al. Errors-in-variables identification of noisy moving average models , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).
[101] Wei Xing Zheng. Fast adaptive identification of autoregressive signals subject to noise , 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).
[102] Paulo Sergio Ramirez,et al. Fundamentals of Adaptive Filtering , 2002 .
[103] R. Kumaresan,et al. Improved spectral resolution , 1980, Proceedings of the IEEE.
[104] Wei Xing Zheng,et al. On unbiased identification of autoregressive signals with noisy measurements , 2015, 2015 IEEE International Symposium on Circuits and Systems (ISCAS).
[105] R. Kumaresan,et al. Estimating the parameters of exponentially damped sinusoids and pole-zero modeling in noise , 1982 .
[106] Md. Kamrul Hasan,et al. Identification of autoregressive signals in colored noise using damped sinusoidal model , 2003 .
[107] W.-X. Zheng. Estimation of the parameters of autoregressive signals from colored noise-corrupted measurements , 2000, IEEE Signal Processing Letters.
[108] James A. Cadzow. Autoregressive Moving Average Spectral Estimation: A Model Equation Error Procedure , 1981, IEEE Transactions on Geoscience and Remote Sensing.
[109] J. Capon. High-resolution frequency-wavenumber spectrum analysis , 1969 .