Optimal Quantum Measurement Design on Speech Signal: Blind Minimax Estimator Improving MSE Over LS Estimators
Journal Title: International Journal of Electronics Communication and Computer Technology - Year 2012, Vol 2, Issue 6
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.
Authors and Affiliations
S. Karthikeyan| Associate Professor ,Dept. of ECE, P.S.N.A. College of Engg. and Tech Dindigul, India, P. Ganesh Kumar| Professor, Department of ECE, KLN College of Engg, Madurai, India, S. Sasikumar| Head&Dean,Department of ECE, Jayaram College of Engg and Tech, Triruchirappalli, India
Keywords
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