TR2020-052

Robust Parameter Estimation of Contaminated Damped Exponentials


Parameter estimation of damped exponential signals has wide applications including fault detection and system parameter identification, etc. However, existing methods for estimating parameters of damped exponentials are either sensitive to noise or restricted to dealing with a certain type of noise such as Gaussian noise. In this paper we aim to estimate parameters of damped exponentials from contaminated signal, i.e., a mixture of damped exponentials, random Gaussian noise, and spike interference. We propose two robust approaches, a convex one solved by the alternating direction method of multipliers (ADMM) and a non-convex one solved by coordinate descent, to recovering a low-rank Hankel matrix of damped exponentials from noisy measurements for further parameter estimation using the matrix pencil technique. Numerical experiments show that our proposed methods outperform classical ones in detecting small damped fault signatures from noisy measurements. While the convex approach is amenable to theoretical analysis and global convergence guarantees, the non-convex one exhibits more robustness and computational efficiency

 

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