Fast Algorithms for Iterative Adaptive Approach Spectral Estimation Techniques

This paper presents computationally efficient implementations for Iterative Adaptive Approach (IAA) spectral estimation techniques for uniformly sampled data sets. By exploiting the methods inherent low displacement rank, together with the development of suitable Gohberg-Semencul representations, and the use of data dependent trigonometric polynomials, the proposed implementations are shown to offer a reduction of the necessary computational complexity with at least one order of magnitude. Numerical simulations together with theoretical complexity measures illustrate the achieved performance gain.