TY - JOUR
T1 - 低信噪比下昆虫振翅频率提取方法研究
AU - Chang, Xinyue
AU - Wang, Rui
AU - Zhang, Tianran
N1 - Publisher Copyright:
© 2021 Editorial Board of Journal of Signal Processing. All rights reserved.
PY - 2021/11
Y1 - 2021/11
N2 - Wingbeat frequency is one of the important parameters of insect radar for insect target recognition. Because of the weakness of insect wingbeat signal, in high background noise, the echo has low signal-to-noise ratio (SNR) and weak signal strength, which affects the accuracy of insect wing vibration frequency extraction. Aiming at the problem of low extraction accuracy of insect wingbeat frequency under low SNR, in this paper, we proposed an extraction method of insect wingbeat frequency under low SNR. The method used the multi-resolution analysis characteristics of wavelet transform to analyze the relationship between the echo wavelet coefficients and the noise wavelet coefficients at each scale, determined the wavelet decomposition level, determined the wavelet threshold and perform targeted noise reduction on the signal. Then the insect wingbeat frequency was extracted by using the time-frequency analysis, the accuracy of which is improved. The results of simulation analysis and experimental data were processed to verify the validity of the proposed method.
AB - Wingbeat frequency is one of the important parameters of insect radar for insect target recognition. Because of the weakness of insect wingbeat signal, in high background noise, the echo has low signal-to-noise ratio (SNR) and weak signal strength, which affects the accuracy of insect wing vibration frequency extraction. Aiming at the problem of low extraction accuracy of insect wingbeat frequency under low SNR, in this paper, we proposed an extraction method of insect wingbeat frequency under low SNR. The method used the multi-resolution analysis characteristics of wavelet transform to analyze the relationship between the echo wavelet coefficients and the noise wavelet coefficients at each scale, determined the wavelet decomposition level, determined the wavelet threshold and perform targeted noise reduction on the signal. Then the insect wingbeat frequency was extracted by using the time-frequency analysis, the accuracy of which is improved. The results of simulation analysis and experimental data were processed to verify the validity of the proposed method.
KW - extraction of insect wingbeat frequency
KW - low signal-to-noise ratio
KW - wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=85186091549&partnerID=8YFLogxK
U2 - 10.16798/j.issn.1003-0530.2021.11.006
DO - 10.16798/j.issn.1003-0530.2021.11.006
M3 - 文章
AN - SCOPUS:85186091549
SN - 1003-0530
VL - 37
SP - 2061
EP - 2068
JO - Journal of Signal Processing
JF - Journal of Signal Processing
IS - 11
ER -