Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-25T15:08:53.860Z Has data issue: false hasContentIssue false

Frequency Domain Design Method of Wavelet Basis Based on Pulsar Signal

Published online by Cambridge University Press:  16 June 2020

Sihai You
Affiliation:
(Department of Missile Engineering, Rocket Force University of Engineering, Xi'an, Shaanxi, China)
Hongli Wang*
Affiliation:
(Department of Missile Engineering, Rocket Force University of Engineering, Xi'an, Shaanxi, China)
Yiyang He
Affiliation:
(Department of Missile Engineering, Rocket Force University of Engineering, Xi'an, Shaanxi, China)
Qiang Xu
Affiliation:
(Qingzhou High-Tech Research Institute, Shandong, China)
Lei Feng
Affiliation:
(Department of Missile Engineering, Rocket Force University of Engineering, Xi'an, Shaanxi, China)
*

Abstract

During pulsar navigation, the high-frequency noise carried by the pulsar profile signal reduces the accuracy of the pulse TOA (Time of Arrival) estimation. At present, the main method to remove signal noise by using wavelet transform is to redesign the function of the threshold and level of wavelet transform. However, the signal-to-noise ratio and other indicators of the filtered signal need to be further optimised, so a more appropriate wavelet basis needs to be designed. This paper proposes a wavelet basis design method based on frequency domain analysis to improve the denoising effect of pulsar signals. This method first analyses the pulsar contour signal in the frequency domain and then designs a Crab pulsar wavelet basis (CPn, where n represents the wavelet basis length) based on its frequency domain characteristics. In order to improve the real-time performance of the algorithm, a wavelet lifting scheme is implemented. Through simulation, this method analyses the pulsar contour signal data at home and abroad. Results show the signal-to-noise ratio can be increased by 4 dB, the mean square error is reduced by 61% and the peak error is reduced by 45%. Therefore, this method has better filtering effect.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Becker, W., Bernhardt, M. G. and Jessner, A. (2013). Autonomous Spacecraft Navigation with Pulsars, Physics. doi: 10.2420/AF07.2013.11.CrossRefGoogle Scholar
Di, Y., Xu, L. and Zhenhua, X. (2007). Wavelet denoising algorithm based on fuzzy threshold for pulsar signal. Journal of Xi'an Jiaotong University, 41(10), 11931196.Google Scholar
Fang, H. Y., Liu, B., Li, X. P., Sun, H. F., Xue, M. F., Shen, L. R. and Zhu, J. P. (2016). Time delay estimation method of X-ray pulsar observed profile based on the optimal frequency band. Acta Physica Sinica, 65(11), 19.Google Scholar
Li, W., Xi-zheng, K. and Guang-ren, N. (2008). Research on noise reduction for millisecond pulsar signal based on wavelet transform. Astronomical Research &Technology, 5(1), 4954.Google Scholar
Liu, Z., He, Z., Guo, W. and Tang, Z. (2016). A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery. ISA Transactions, 61, 211220. https://www.journals.elsevier.com/isa-transactions.CrossRefGoogle ScholarPubMed
Sweldens, W. I. M. (1998). The lifting scheme: a construction of second generation wavelets. SIAM Journal on Mathematical Analysis, 29(2), 511546.CrossRefGoogle Scholar
Wei, G., Yue, Z., Wei, W., Haiyu, L. and Ya, Z. (2012). Research on real-time de-noising of FOG based on second generation wavelet transform. Chinese Journal of Scientific Instrument, 33(4), 774780.Google Scholar
Xiao-ming, Z., Fu-cheng, L. and Yuan-yan, T. (2006). Pulsar signal denoising based on wavelet transformation. Acta Astronomica Sinica, 47(3), 238335.Google Scholar
Xu, Q., Wang, H., Feng, L., Jiang, W., You, S. and He, Y. (2018a). An improved augmented X-ray pulsar navigation algorithm based on the norm of pulsar direction error. Advances in Space Research, 62(11), 31873198.CrossRefGoogle Scholar
Xu, Q., Wang, H., Feng, L., You, S. and He, Y. (2018b). A novel X-ray pulsar integrated navigation method for ballistic aircraft. Optik, 175, 2838.CrossRefGoogle Scholar
Xue, M. F., Li, X. P., Fu, L. Z., Liu, X. P., Sun, H. F. and Shen, L. R. (2016). Denoising of X-ray pulsar observed profile in the undecimated wavelet domain. Acta Astronautica, 118, 110.CrossRefGoogle Scholar
You, S., Wang, H., He, Y., Xu, Q., Lu, J. and Feng, L. (2018). Pulsar profile construction based on double-redundant-dictionary and same-scale L1-Norm compressed sensing. Optik, 164, 617623.CrossRefGoogle Scholar
Zhe, S. U., Xu, L., Yo, W., Zhen, X. I. E. and Nan, L. U. O. (2010). Pulsar weak signal denoising based on improved wavelet spatial correlation filtering. Systems Engineering and Electronics, 32(12), 25002505.Google Scholar