TY - GEN
T1 - A Novel Parameter Estimation Algorithm for UAV by the Combined CVMD and TMSST Technique
AU - Yitong, Pan
AU - Ying-Chun, Li
AU - Haozhen, Bai
AU - Xiang, Feng
AU - Zhengjie, Zhou
AU - Zhiquan, Zhou
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Most existing parameter estimations for small unmanned aerial vehicles (UAV) have been transmuted into the extraction of micro-Doppler features. Linear time-frequency analysis methods represented by Short-Time Fourier Transform (STFT) and Wavelet Transform (WT) fall short in the flexibility of adaptation to the time window widths. Meanwhile, nonlinear approaches of time-frequency analysis, such as the Pseudo Wigner-Ville Distribution (PWVD), are verified arduous to eliminate the influence of cross terms. These methods apparently possess innate shortcomings. In this paper, we make the first attempt to introduce a combination algorithm to estimate the frequency of UAV rotors. Firstly, we employ Complex Variational Mode Decomposition (CVMD) to decompose the echo signals. And then, we adopt Time-Reassigned Multisynchrosqueezing Transform (TMSST) to process high-frequency components. Secondly, we use Time-domain Fast Fourier Transform (FFT) to obtain the CFD spectrum. Finally, we use Singular Value Decomposition (SVD) to estimate the rotor speed. The simulation and the experimental data show that our proposed combination algorithm not only improves the time-frequency energy aggregation, but improves the accuracy of blades rotation estimation.
AB - Most existing parameter estimations for small unmanned aerial vehicles (UAV) have been transmuted into the extraction of micro-Doppler features. Linear time-frequency analysis methods represented by Short-Time Fourier Transform (STFT) and Wavelet Transform (WT) fall short in the flexibility of adaptation to the time window widths. Meanwhile, nonlinear approaches of time-frequency analysis, such as the Pseudo Wigner-Ville Distribution (PWVD), are verified arduous to eliminate the influence of cross terms. These methods apparently possess innate shortcomings. In this paper, we make the first attempt to introduce a combination algorithm to estimate the frequency of UAV rotors. Firstly, we employ Complex Variational Mode Decomposition (CVMD) to decompose the echo signals. And then, we adopt Time-Reassigned Multisynchrosqueezing Transform (TMSST) to process high-frequency components. Secondly, we use Time-domain Fast Fourier Transform (FFT) to obtain the CFD spectrum. Finally, we use Singular Value Decomposition (SVD) to estimate the rotor speed. The simulation and the experimental data show that our proposed combination algorithm not only improves the time-frequency energy aggregation, but improves the accuracy of blades rotation estimation.
KW - CVMD
KW - TMSST
KW - UAV
KW - parameter estimation
KW - time-frequency analysis
UR - https://www.scopus.com/pages/publications/85172985218
U2 - 10.1109/iWEM58222.2023.10234937
DO - 10.1109/iWEM58222.2023.10234937
M3 - 会议稿件
AN - SCOPUS:85172985218
T3 - 2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023
SP - 378
EP - 384
BT - 2023 IEEE International Workshop on Electromagnetics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023
Y2 - 15 July 2023 through 18 July 2023
ER -