Robust and Iterative Adaptive Signal Processing

Book excerpt: Moreover, a model-order selection tool, the generalized information criterion (GIC) can be used in conjunction with ST-IAA to further improve the spectrogram quality. Finally, we present several iterative adaptive signal processing approaches to aeroacoustic noise analysis. One of the approaches is based on optimizing the maximum likelihood (ML) criterion via using the Newton's method. The other approaches, referred to as the Frobenius norm (FN) and Rank-1 methods, employ the cyclic optimization algorithm to solve the problem. We also derive the Cram\U+00b4\er-Rao Bounds (CRB) of the unbiased source power estimates. The proposed methods are evaluated using both simulated and measured data. The numerical examples show that these algorithms significantly outperform the existing least squares approach and provide accurate power estimates even under low SNR conditions. Furthermore, the MSEs of the so-obtained estimates are close to the corresponding CRB, especially when the number of data samples is large. The experimental results show that the power estimates obtained by the proposed approaches are consistent with one another.