Variance Of White Gaussian Noise Research Articles

Noise Parameter Estimation From Quantized Data

In this paper, the parametric estimation of the variance of white Gaussian noise is considered when available data are obtained from a quantized noisy stimulus. The Crameacuter-Rao lower bound is derived, and the statistical efficiency of a maximum-likelihood parametric estimator is discussed, along with the estimation algorithm proposed in IEEE Standard 1241

Chandrasekhar-type filter using covariance information for white Gaussian plus colored observation noise

This paper proposes recursive least-squares (RLS) Chandrasekhar-type filtering equations using the covariance information in the case of white Gaussian plus colored observation noise in linear continuous-time wide-sense stationary stochastic systems. Here, it is assumed that the system matrices in the state-space models for the signal and the colored noise, the cross-variance functions of the state variables for the signal and the colored noise with the observed value, the observation vectors for the signal and the colored noise, the variance of white Gaussian noise and the observed value are known. The number of differential equations included in the current Chandrasekhar-type filter is less than the previous filter using the covariance information. The Chandrasekhar-type equations are derived based on the invariant imbedding method. The Chandrasekhar-type filter brings numerically efficient algorithm in computation time.

Optimal filtering algorithm using covariance information in linear discrete-time distributed parameter systems

This paper presents an optimal discrete-time filtering algorithm using covariance information in linear distributed parameter systems. It is assumed that observation noise is a white Gaussian process. The autocovariance function of the signal, the variance of white Gaussian noise and the observed value are used in the filtering algorithm. It is an advantage that the current filtering algorithm is applied to the case where a difference equation, which generates a signal process, is unknown in linear stochastic distributed parameter systems.