High resolution spectral estimation using a specially constrained adaptive notch filter

Abstract

This paper presents a high resolution spectral estimation technique using a specially constrained adaptive notch filter to estimate and resolve two closely spaced dominant frequencies buried in noise. The notch filter parametrization is an autoregressive moving average model which is implemented in cascaded form. A computationally simple gradient-based adaptive algorithm was applied and found to provide a robust solution to the problem of parameter estimation. Error surface analysis is included and clearly indicates that the algorithm has guaranteed convergence. In addition the analysis provides valuable insights into the appropriate filter and adaption coefficient parameter selection. Extensive experimental testing has been undertaken and results indicate superior performance over existing techniques such as the fast Fourier transform. Typical results are included to demonstrate the capabilities of the proposed technique.