WU Shuang, TIAN Jing, CUI Wei. A Novel Parameter Estimation Algorithm for DSSS Signals Based on Compressed Sensing[J]. Chinese Journal of Electronics, 2015, 24(2): 434-438. doi: 10.1049/cje.2015.04.035
Citation: WU Shuang, TIAN Jing, CUI Wei. A Novel Parameter Estimation Algorithm for DSSS Signals Based on Compressed Sensing[J]. Chinese Journal of Electronics, 2015, 24(2): 434-438. doi: 10.1049/cje.2015.04.035

A Novel Parameter Estimation Algorithm for DSSS Signals Based on Compressed Sensing

doi: 10.1049/cje.2015.04.035
Funds:  This work was supported by the Foundation of Shanghai Aerospace Science and Technology (No.SAST201215) and the Program for New Century Excellent Talents in University (No.NCET-13-0034).
More Information
  • Corresponding author: TIAN Jing was born in Shandong province, 1984. She received the B.S. and M.S. degrees both in electronic engineering from Xidian University, in 2006 and 2009, respectively. In 2009, she joints the Radar Research Institute in Beijing Institute of Technology to pursue her Ph.D. degree. Her research interests include moving-target detection, parameter estimation and imaging. (Email:10905046@bit.edu.cn)
  • Publish Date: 2015-04-10
  • A novel parameter estimation algorithm based on Compressed sensing (CS) for the Direct sequences spread spectrum (DSSS) signals in high dynamic environments is proposed. In this algorithm, Fractional Fourier transform (FrFT) is first employed to estimate Doppler frequency rate, followed by the quadric phase term compensation. The compensation results are divided into several segments with equal length and coherent integration is carried out within each segment respectively. A convex optimization algorithm is applied to estimate the velocity and initial range of the target simultaneously based on the sparsity of target in the code phase domain. The proposed algorithm is capable of overcoming the limitation of Doppler frequency ambiguity and obtaining the accurate parameter estimates without correcting the code phase drift. Simulation results are presented to demonstrate the validity of the proposed algorithm.
  • loading
  • Y.L. Zhang, et al., “Research on pseudo code acquisition method in aerospace spread spectrum TT&C system”, Acta Electronica Sinica, Vol.39, No.6, pp.1471-1476, 2011. (in Chinese)
    C.X. Li, F.X. Wang and G.R. Guo, “Correlation of PN spread spectrum signal under first-order dynamics”, Acta Electronica Sinica, Vol.35, No.9, pp.1789-1793, 2007. (in Chinese)
    R. Tao, N. Zhang and Y. Wang, “Analysing and compensating the effects of range and Doppler frequency migrations in linear frequency modulation pulse compression radar”, IEE Proc. Radar Sonar Navig., Vol.5, No.1, pp.12-22, 2011.
    T.J. Abatzoglou, “Fast maximum likelihood joint estimation of frequency and frequency rate”, Proc. of International Conference on Acoustics, Speech, and Signal Processing, Tokyo, Japan, pp.1409-1412, 1986.
    L. Huang, S.L. Wu and E.K. Mao, “Recursion subspace-based method for bearing estimation: a comparative study”, Chinese Journal of Electronics, Vol.19, No.3, pp.477-480, 2010.
    S. Barbarossa and A. Farina, “Detection and imaging of moving objects with synthetic aperture radar. 2. Joint time-frequency analysis by Wigner-Ville distribution”, IEE Proc. Inst. Elect. Eng. F, Vol.139, No.1, pp.89-87, 1992.
    H.B. Sun, et al., “Application of the fractional Fourier transform to moving target detection in Airborne SAR”, IEEE Trans. Aerosp. Electron. Syst., Vol.38, No.4, pp.1416-1426, 2002.
    J. Tian, W. Cui and S.L. Wu, “A novel method for parameter estimation of space moving targets”, IEEE Geosci. Remote Sens. Lett., Vol.11, No.2, pp.389-393, 2014.
    R.P. Perry, R.C. DiPietro and R. Fante, “SAR imaging of moving targets”, IEEE Trans. Aerosp. Electron. Syst., Vol.35, No.1, pp.188-200, 1999.
    J.G. Yang, X.T. Huang, J. Thompson, T. Jing and Z.M. Zhou, “Low-frequency ultra-wideband synthetic aperture radar ground moving target imaging”, IET Radar, Sonar, Navig., Vol.5, No.9, pp.994-1001, 2011.
    J.Wang and S.H. Zhang, “Study on the motion compensation of range migration for weak moving target detection”, Acta Electronica Sinica, Vol.28, No.12, pp.56-59, 2000. (in Chinese)
    A.L. Warrick and P.A. Delaney, “Detection of linear features using a localized Radon transform”, Proc. of the 30th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, pp.1245-1249, 1996.
    D.L. Donoho, “Compressed sensing”, IEEE Trans. Inf. Theory, Vol.52, No.44, pp.1289-1306, 2006.
    Q.S. Wu, et al., “Motion parameter estimation in the SAR system with low PRF sampling”, IEEE Geosci. Remote Sens. Lett., Vol.7, No.3, pp.450-454, 2010.
    B. Fan, et al., “Discrete chirp-Fourier transform-based acquisition algorithm for weak global positioning system L5 signals in high dynamic environments”, IET Radar, Sonar, Navig., Vol.7, No.7, pp.736-746, 2013.
    R.E. Carrillo, et al., “Robust sampling and reconstruction methods for sparse signals in the presence of impulsive noise”, IEEE J. Sel. Topics Signal Process., Vol.4, No.2, pp.392-408, 2010.
    E.J. Candes and T. Tao, “Near-optimal signal recovery from random projections: Universal encoding strategies?”, IEEE Trans. Inf. Theory, Vol.52, No.2, pp.5406-5425, 2006.
    A.T. Figueiredo, R.D. Nowak and S.J. Wright, “Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems”, IEEE J. Sel. Topics Signal Process., Vol.1, No.4, pp.586-597, 2007.
    H.K. Luo, Y.Q. Wang, Z.H. Ma and S.L. Wu, “A segmentation motion compensation-based long term integration method for DSSS signal”, Proc. of the 11th International Conference on Signal Processing, Beijing, China, pp.1287-1290, 2012.
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (241) PDF downloads(1038) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint