An Efficient Recursive Least Square (ERLS) Algorithm for Spectral Estimation with the Aid of Wavelet and Artificial Intelligence

Abstract

The spectral estimation technique is used for time frequency signal analysis, speech processing, and other signal processing applications. Some drawbacks of RLS algorithm are that it requires high computational power and the output obtained is numerically instability. So, the spectral efficiency of the signal is affected and a power error occurs in the estimator. In this paper, an efficient recursive least square (ERLS) algorithm is proposed for improving the power signal spectral estimation. The proposed ERLS algorithm is the combination of wavelet algorithm and artificial neural network (ANN). The wavelet algorithm is used to extract the frequency components of the power signal. Then, using the neural network, the power error of the linear and non-linear signals is determined. So, the complexity and computational time of spectral estimation are reduced. To analyze the estimation performance of ERLS algorithm, the capon and amplitude and phase estimation (APES) based spectral analysis methods are used. The spectral estimation capability of the proposed ERLS algorithm is determined by the error weight of the signal. The proposed ERLS algorithm is implemented in the MATLAB platform and the output performance are evaluated.

Keywords:Signal processing, spectral estimation, ERLS algorithm, wavelet algorithm, ANN