A new estimation algorithm has been developed here, which refers to covariance shaping least square estimation (CSLS) based on the quantum mechanical concepts and constraints. The algorithm has been applied to ARMA, complex exponential, sine, cosine and sinc models with various parameter values. The same models can be applied with white Gaussian noise, which estimates the bias in the parameter and the validity of the uncertainty can be analysed. For optimal quantum measurement design, the performance of the CSLS estimator is developed, discussed and compared with LS, Shrunken and Ridge estimators for different applications. The results suggest that the CSLS estimator can outperform from others at low-to-moderate signal-to-noise ratio.
Inspec keywords: signal processing; autoregressive moving average processes; least squares approximations; maximum likelihood estimation; covariance analysis; quantum computing; AWGN
Other keywords:
Subjects: Interpolation and function approximation (numerical analysis); Other topics in statistics; Quantum computing theory; Interpolation and function approximation (numerical analysis); Signal processing and detection; Other topics in statistics; Signal processing theory
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