On global identifiability for arbitrary model parametrizations
Automatica (Journal of IFAC)
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Random Data: Analysis and Measurement Procedures
Random Data: Analysis and Measurement Procedures
On the Euclidean Distance of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering and System Identification: A Least Squares Approach
Filtering and System Identification: A Least Squares Approach
Determining the initial states in forward-backward filtering
IEEE Transactions on Signal Processing
Wavelet thresholding techniques for power spectrum estimation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory - Part 2
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Image Denoising Using Derotated Complex Wavelet Coefficients
IEEE Transactions on Image Processing
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It was shown recently that parameter estimation can be performed directly in the time-scale domain by isolating regions wherein the prediction error can be attributed to the error of individual dynamic model parameters [1]. Based on these single-parameter equations of the prediction error, individual model parameters error can be estimated for iterative parameter estimation. An added benefit of this parameter estimation method, besides its unique convergence characteristics, is the added capacity for direct noise compensation in the time-scale domain. This paper explores this benefit by introducing a noise compensation method that estimates the distortion by noise of the prediction error in the time-scale domain and incorporates that as a confidence factor to bias the estimation of individual parameters error. This method is shown to improve the precision of the estimated parameters when the confidence factors accurately represent the noise distortion of the prediction error.