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

WU Shuang; TIAN Jing; CUI Wei

doi:10.1049/cje.2015.04.035

Volume 24 Issue 2

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WU Shuang, TIAN Jing, CUI Wei, “A Novel Parameter Estimation Algorithm for DSSS Signals Based on Compressed Sensing,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 434-438, 2015, 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,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 434-438, 2015, doi: 10.1049/cje.2015.04.035

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

doi: 10.1049/cje.2015.04.035

Radar Research Laboratory, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

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

Abstract

Abstract

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.
- Compressed sensing (CS),
- Fractional Fourier transform (FrFT),
- Direct sequences spread spectrum (DSSS),
- Doppler frequency ambiguity

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References(19)

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