Incremental Methods for Robust Local Subspace Estimation

2016 ◽  
pp. 11-1-11-37
Keyword(s):  
Incremental Methods ◽  
Subspace Estimation

High-Accuracy Fundamental Frequency Detection in Power System

2011 ◽  
Vol 383-390 ◽  
pp. 4962-4966
Author(s):  
Ling Li ◽  
Guo Bin Jin ◽  
Shao Ping Huang ◽  
Xiao Peng
Keyword(s):  
Power System ◽  
Fundamental Frequency ◽  
Frequency Measurement ◽  
Least Square ◽  
Signal Model ◽  
Least Square Estimation ◽  
Signal Process ◽  
Frequency Detection ◽  
Novel Method ◽  
Subspace Estimation

A novel method on frequency measurement based on improved TLS-ESPRIT (total least square estimation of signal parameters via rotational invariance techniques) is proposed in this paper with the research on fundamental frequency measurement in power system. TLS-ESPRIT is belong to subspace estimation in modern signal process. Noise is included in signal model, so it is independent on noise. But the same multi-poles cannot be taken when signal is in noise and based on TLS-ESPRIT. Multiple poles restoring is presented to take the true poles accurately. It is revealed that fundamental frequency is detected accurately in harmonics, interharmonics, noise and frequency fluctuations and better anti-noise ability in particular better adaptiveness on time varying signal in amplitude by simulation results.

scholarly journals Denoising of pre‐stack seismic data using subspace estimation methods

2018 ◽  
Vol 12 (8) ◽  
pp. 992-999 ◽  
Author(s):  
Karthikeyan Elumalai ◽  
Shailesh Kumar ◽  
Brejesh Lall ◽  
Rakesh Kumar Patney
Keyword(s):  
Seismic Data ◽  
Estimation Methods ◽  
Subspace Estimation

scholarly journals LARGE INFORMATION PLUS NOISE RANDOM MATRIX MODELS AND CONSISTENT SUBSPACE ESTIMATION IN LARGE SENSOR NETWORKS

2012 ◽  
Vol 01 (02) ◽  
pp. 1150006 ◽  
Author(s):  
WALID HACHEM ◽  
PHILIPPE LOUBATON ◽  
XAVIER MESTRE ◽  
JAMAL NAJIM ◽  
PASCAL VALLET
Keyword(s):  
Covariance Matrix ◽  
Estimation Method ◽  
Singular Values ◽  
Subspace Methods ◽  
Localization Function ◽  
Random Matrix Models ◽  
Order Of Magnitude ◽  
Source Signal ◽  
Subspace Estimation ◽  
Empirical Covariance

In array processing, a common problem is to estimate the angles of arrival of K deterministic sources impinging on an array of M antennas, from N observations of the source signal, corrupted by Gaussian noise. In the so-called subspace methods, the problem reduces to estimate a quadratic form (called "localization function") of a certain projection matrix related to the source signal empirical covariance matrix. The estimates of the angles of arrival are then obtained by taking the K deepest local minima of the estimated localization function. Recently, a new subspace estimation method has been proposed, in the context where the number of available samples N is of the same order of magnitude than the number of sensors M. In this context, the traditional subspace methods tend to fail because they are based on the empirical covariance matrix of the observations which is a poor estimate of the source signal covariance matrix. The new subspace method is based on a consistent estimator of the localization function in the regime where M and N tend to +∞ at the same rate. However, the consistency of the angles estimator was not addressed, and the purpose of this paper is to prove this consistency in the previous asymptotic regime. For this, we prove the property that the singular values of M × N Gaussian information plus noise matrix escape from certain intervals is an event of probability decreasing at rate [Formula: see text] for all p. A regularization trick is also introduced, which allows to confine these singular values into certain intervals and to use standard tools as Poincaré inequality to characterize any moments of the estimator. These results are believed to be of independent interest.

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