Author(s): S.Karthikeyan | P.Ganesh Kumar | S.Sasikumar
Journal: International Journal of Electronics Communication and Computer Technology
ISSN 2249-7838
Volume: 2;
Issue: 6;
Start page: 247;
Date: 2012;
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Keywords: Quantum measurements | Minimax Estimator | White Gaussian noise | linear regression | Mean square error | Biased Estimation
ABSTRACT
We consider the problem of estimating an unknown, deterministic speech signal parameters based on quantum measurements corrupted by white Gaussian noise. We design and analyze blind minimax estimator (BME), which consist of a bounded parameter set. Using minimax estimator, the parameter set is itself estimated from quantum measurements. Thus, our approach does not require any prior knowledge of bounded parameters, and the designed estimator can be applied to any linear regression problem. We demonstrate analytically that the BMEs strictly dominate the least-square (LS) estimator, i.e., they achieve lower mean-squared error (MSE) for any speech signal. Our approach can be readily compared with wide class of non-linear estimators like James Stein’s estimator, which is defined for white noise. The result suggest that over a wide range of samples and signal to noise ratio the mean square error for Ellipsoidal Blind Minimax Estimator(EBME) is lower when compared with linear and non-linear estimators.
Journal: International Journal of Electronics Communication and Computer Technology
ISSN 2249-7838
Volume: 2;
Issue: 6;
Start page: 247;
Date: 2012;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: Quantum measurements | Minimax Estimator | White Gaussian noise | linear regression | Mean square error | Biased Estimation
ABSTRACT
We consider the problem of estimating an unknown, deterministic speech signal parameters based on quantum measurements corrupted by white Gaussian noise. We design and analyze blind minimax estimator (BME), which consist of a bounded parameter set. Using minimax estimator, the parameter set is itself estimated from quantum measurements. Thus, our approach does not require any prior knowledge of bounded parameters, and the designed estimator can be applied to any linear regression problem. We demonstrate analytically that the BMEs strictly dominate the least-square (LS) estimator, i.e., they achieve lower mean-squared error (MSE) for any speech signal. Our approach can be readily compared with wide class of non-linear estimators like James Stein’s estimator, which is defined for white noise. The result suggest that over a wide range of samples and signal to noise ratio the mean square error for Ellipsoidal Blind Minimax Estimator(EBME) is lower when compared with linear and non-linear estimators.
