Operational modal parameter identification is a tough problem in aerospace engineering due to the complex mechanics\nenvironment, various noises, and limited computational resources. In this paper, a novel, recursive, robust, and highefficiency\nmodal parameter identification approach is proposed for this issue. The kernelized time-dependent autoregressive\nmoving average (TARMA) model is adopted to model the nonstationary responses, a recursive estimator is\nestablished based on the maximum correntropy criterion, and sliding-window technique is applied to fix the computational\ncomplexity, which ensures the approach its estimation accuracy, robustness, and high efficiency. Finally, steps of\nthe identification procedure and model selection are presented. An experimental scheme is proposed for validation, and\nthe proposed approach is comparatively assessed against the classical recursive pseudo-linear regression TARMA method\nvia Monte Carole tests of a time-varying experimental system. The results of the comparative study demonstrate that the\nproposed method achieves similar estimation accuracy and higher computation efficiency under the Gaussian environment.\nMoreover, a superior estimation accuracy and enhanced robustness are rendered under additive non-Gaussian\nimpulsive noise.
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