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Robust Signal Extraction Based on Time-Frequency Joint Analysis and GRNN for a Laser SMI System

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In pursuit of vibration recovery, with subwavelength precision based on laser self-mixing interferometry (SMI), the estimation of two key parameters, i.e., the optical feedback parameter C and linewidth enhancement factor α , is of vital importance. However, the above process is not ideal in terms of accuracy and real-time performance. Aiming to overcome these deficiencies, a time-domain-based frequency-domain processing method (TFPM) has been proposed for the SMI system in this paper, which realizes robust and fast extraction of the vibration signals without the estimation of the parameters and does not require fringe detection. The method achieves the extraction of motion information via a mathematical transformation, namely, short-time Fourier transform (STFT), in which the limitations caused by STFT are alleviated by using generalized regression neural network (GRNN). In processing the SMI signal of the target with non-stationary motion, the TFPM finally achieves an expected root-mean-square (RMS) error of 0.24 % , which demonstrates the high performance of the proposed method.

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Ketersediaan

Barcode		Tipe Koleksi	Nomor Panggil	Lokasi	Status
art149340	null	Artikel		Gdg9-Lt3	Tersedia namun tidak untuk dipinjamkan - No Loan

Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.40 No.22 November 15 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 7229-7235

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

SHORT-TIME FOURIER TRANSFORM
TIME-FREQUENCY ANALYSIS
GENERALIZED REGRESSION NEURAL NETWORK
SELF-MIXING

Info Detail Spesifik

DOI: 10.1109/JLT.2022.3201098

Pernyataan Tanggungjawab

Shize Ge, Xinyu Kong, Desheng Zhu, Hanqiao Chen, Yunxiu Lin, Xiulin Wang, Wencai Huang

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