Tianhong Ma
ABSTRACT
At low SNRs, the analog signal will be swamped by noise. Aiming at the low estimation accuracy of the traditional signal bandwidth estimation algorithms, a signal bandwidth estimation method based on the Wavelet reconstruction is proposed in this paper. Firstly, the influence of noise is reduced by means of data segmentation cross-correlation. Secondly, the envelope of signal amplitude spectrum is extracted by the wavelet low-frequency reconstruction. Finally, according to its envelope, the boundary can be found of signal amplitude spectrum by the difference operation. The estimation is completed of the signal zero-crossing bandwidth. In this method, the wavelet reconstruction is applied to signal bandwidth estimation for the first time, which can reduce the negative impact of signal randomness on the spectrum envelop. In addition, the extreme point searching algorithm is designed to confirm the upper and lower frequency bands of the reconstructed spectrum envelope, which is easy to implement and can be directly applied in the engineering field. The experimental results show that the proposed method is robust and can achieve good results at low SNRs.
- Jin Pengfei. Research on Non-cooperative Multi-signal Detection and Carrier Frequency and Bandwidth Estimation [D]. China Academy of Engineering Physics,2017.Google Scholar
- Ge Fengxiang, Meng Huadong, Peng Yingning, Wang Xiutan. Clutter center and spectral width estimation methods [J]. Journal of Tsinghua University (Science & Technology), 2002(07):941-944.Google Scholar
- M. Niedźwiecki, M. Ciołek and Y. Kajikawa, "On adaptive selection of estimation bandwidth for analysis of locally stationary multivariate processes," 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 4860-4864.Google Scholar
- L. G. Weiss, "Wavelets and wideband correlation processing," in IEEE Signal Processing Magazine, vol. 11, no. 1, pp. 13-32, Jan. 1994.Google ScholarCross Ref
- K. Tamayama, M. Ohta and M. Taromaru, "Signal bandwidth estimation with energy detector based on windowed FFT for cognitive radio system," 2015 International Conference on Information and Communication Technology Convergence (ICTC), 2015, pp. 435-437.Google Scholar
- D. Rzepka, M. Pawlak, D. Kościelnik and M. Miśkowicz, "Bandwidth Estimation From Multiple Level-Crossings of Stochastic Signals," in IEEE Transactions on Signal Processing, vol. 65, no. 10, pp. 2488-2502, 15 May15, 2017.Google ScholarDigital Library
- Ye Hui. Research on broadband spectrum detection technology and FPGA implementation [D]. University of Electronic Science and Technology of China, 2020.Google Scholar
- Yan Fucheng. Research on blind demodulation technology of OFDM signal [D]. University of Electronic Science and Technology of China, 2019.Google Scholar
- Liu baozhou. Research on power spectrum estimation and its improved algorithm based on period graph method [J]. Electronic measurement technology, 2020, 43(05):76-79.Google Scholar
- M. Liu and B. Li, "Bandwidth blind estimation for OFDM," 2016 IEEE International Conference on Digital Signal Processing (DSP), 2016, pp. 181-184.Google Scholar
- Peng Geng, Huang Zhitao, WANG Fenghua, Jiang Wenli. Blind Estimation of satellite communication signal parameters based on curve Fitting [J].System engineering and electronics, 2010, 32(03):450-453.Google Scholar
- Wang Binghe, Gong Anmin, Qu Yi, Guo Yaoting. Automatic bandwidth estimation for orthogonal frequency division multiplexing in low SNR multipath channels [J]. Science technology and engineering, 2015, 15(30):150-154.Google Scholar
- Yang Weichao, Yang Xinquan. Signal bandwidth estimation based on geometry analysis of power spectrum distribution function [J]. Systems engineering and electronics, 2019, 41(05):981-985.Google Scholar
- Sun Zhe, Jiang Weina. Extraction of reflected in-seam wave signal based on wavelet decomposition and reconstruction method [J]. Coal technology, 2019, 38(12):55-57.Google Scholar
- Zhang Junbin, Zou Qiongfen, Zhong Jun, Xu Xiaobin, Lin Jingzhou, Wu Yousheng, Yang Renmilling. Balance Electromagnetic Interference Processing Technology based on Wavelet Reconstruction [J]. Electro-optics & Control, 201, 28(09):94-97.Google Scholar
Index Terms
- Signal Bandwidth Estimation Based on the Wavelet Reconstruction
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