Real-time sinusoidal parameter estimation for damage growth monitoring during ultrasonic very high cycle fatigue tests

Kiser, Shawn L.; Rébillat, Marc; Guskov, Mikhaïl; Ranc, Nicolas

doi:10.1016/j.ymssp.2022.109544

Cited by 8 publications

(1 citation statement)

References 49 publications

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“…It has been shown by benchmarks in the literature, e.g. vibration analysis [24] and power system synchrophasors [25], that ESPRIT exceeded other state-of-the-art alternatives in performance metrics but at a much higher computational cost. Thus, for problems requiring real-time usage, subspace methods would benefit from fast estimations of the signal subspace.…”

Section: Introductionmentioning

confidence: 99%

FFT-ESPRIT: a kernel-based subspace estimator for frequency super-resolution at quasi-linear time complexity

Kiser¹,

Margerit²,

Rébillat³

et al. 2023

Preprint

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<p>We introduce two FFT-based ESPRIT algorithms for line spectral estimation which have lower time complexities than the original ESPRIT algorithm's O(N^3). The preferred method, named FFT-ESPRIT, can be characterized as being a kernel-based subspace estimator that achieves super-resolution at O(N log N) for frequency estimates. First, we demonstrate two estimations of the signal subspace via an integral transformation on the row space of the data matrix and the data matrix itself. The subspace-based methods are approximate in nature, and yet perturbation bounds reveal a noise regime in which FFT-ESPRIT exceeds ESPRIT's performance. We demonstrate the behavior of the algorithm across different SNR regimes and show that the estimated signal subspace is statistically efficient. Numerical simulations show that FFT-ESPRIT is more robust than the ESPRIT algorithm at the very low SNRs, and has a nearly identical performance as ESPRIT at higher SNRs.</p>

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Section: Introductionmentioning

confidence: 99%