Linear Prediction Approach to the Robust Parameter Estimation for the Damped Sinusoids

Zhou, Zhenhua; Liu, Yong; Christensen, Mads Græsbøll; Chen, Kai

doi:10.1109/icsp48669.2020.9320918

Cited by 1 publication

(1 citation statement)

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“…Remark 1. Part of the work has been presented in [35] and [36], where the robust order detection and the LP-based robust parameter estimation are reported for the single-channel sinusoidal signals, respectively. The multi-channel extension and the MAP-based parameter estimation methodology are provided only in this journal version, which naturally includes more comprehensive performance analysis and simulation results.…”

Section: Introductionmentioning

confidence: 99%

Robust Spectral Analysis of Multi-Channel Sinusoidal Signals in Impulsive Noise Environments

Zhou

Christensen

Zhang

2022

IEEE Trans. Signal Process.

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Robust spectral analysis of the sinusoidal signals corrupted by impulsive noise poses a big challenge in the signal processing community. In this paper, we address the issue of robust spectral analysis for multi-channel sinusoidal signals, including order detection and parameter estimation. The successive robust low-rank decomposition is firstly designed to extract the common signal subspace from the multi-channel data matrix. Subsequently, the number of sinusoidal poles is determined with a model order selection criterion, based on the so-obtained subspaces. With the signal order information, the sinusoidal parameters and outliers are jointly estimated according to the maximum a posteriori criterion. To find a robust initial guess of the sinusoidal parameters, an estimator based on robust weighted linear prediction is developed. Additionally, the performance analysis is provided, which includes computational complexity, convergence verification of the sinusoidal parameter estimation, and asymptotic consistency of the signal order detection. Simulation results demonstrate the advantages of the proposed robust spectral analysis framework compared to state-of-the-art schemes.

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Section: Introductionmentioning

confidence: 99%