On Errors-in-variables Estimation with Unknown Noise Variance Ratio
نویسندگان
چکیده
We propose an estimation method for an errors-in-variables model with unknown input and output noise variances. The main assumption that allows identifiability of the model is clustering of the data into two clusters that are distinct in a certain specified sense. We show an application of the proposed method for system identification.
منابع مشابه
Adaptive Signal Detection in Auto-Regressive Interference with Gaussian Spectrum
A detector for the case of a radar target with known Doppler and unknown complex amplitude in complex Gaussian noise with unknown parameters has been derived. The detector assumes that the noise is an Auto-Regressive (AR) process with Gaussian autocorrelation function which is a suitable model for ground clutter in most scenarios involving airborne radars. The detector estimates the unknown...
متن کاملAcoustic correlated sources direction finding in the presence of unknown spatial correlation noise
In this paper, a new method is proposed for DOA estimation of correlated acoustic signals, in the presence of unknown spatial correlation noise. By generating a matrix from the signal subspace with the Hankel-SVD method, the correlated resource information is extracted from each eigen-vector. Then a joint-diagonalization structure is constructed of the signal subspace and basis it, independent...
متن کاملOptimal Errors–in–variables Filtering in the Mimo Case
Abstract: The Errors–in–Variables (EIV) stochastic environment constitutes a superset of most common stochastic environments considered, for instance, in Kalman filtering or in equation–error identification where the process input is assumed as noise–free. Errors– in–variables models assume, on the contrary, the presence of unknown additive noise also on the inputs; the associated filtering pro...
متن کاملA Robust Distributed Estimation Algorithm under Alpha-Stable Noise Condition
Robust adaptive estimation of unknown parameter has been an important issue in recent years for reliable operation in the distributed networks. The conventional adaptive estimation algorithms that rely on mean square error (MSE) criterion exhibit good performance in the presence of Gaussian noise, but their performance drastically decreases under impulsive noise. In this paper, we propose a rob...
متن کاملA Robust Nonparametric Estimation Framework for Implicit Image Models
Robust model fitting is important for computer vision tasks due to the occurrence of multiple model instances, and, unknown nature of noise. The linear errors-in-variables (EIV) model is frequently used in computer vision for model fitting tasks. This paper presents a novel formalism to solve the problem of robust model fitting using the linear EIV framework. We use Parzen windows to estimate t...
متن کامل