Blind Estimation of the Spread Spectrum Sequence of DS-SS Signals Based on Improved Propagator Method

doi:10.3969/j.issn.0372-2112.2020.01.002

您当前的位置：

首页 >

文章列表页 >

Blind Estimation of the Spread Spectrum Sequence of DS-SS Signals Based on Improved Propagator Method

更新时间：2025-07-08

- Blind Estimation of the Spread Spectrum Sequence of DS-SS Signals Based on Improved Propagator Method
- Acta Electronica Sinica Vol. 48, Issue 1, Pages: 9-16(2020)
- 作者机构：
  
  陆军工程大学石家庄校区电子与光学工程系,河北,石家庄,050003
- 作者简介：
- 基金信息：
  
  National Defense Science and Technology Foundation of China (No.2004022);National Natural Science Foundation of China (No.61473033)
- DOI：10.3969/j.issn.0372-2112.2020.01.002
  CLC： TN911
- Published Online：25 January 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
Blind Estimation of the Spread Spectrum Sequence of DS-SS Signals Based on Improved Propagator Method[J]. Acta Electronica Sinica, 2020, 48(1): 9-16.
DOI：

Blind Estimation of the Spread Spectrum Sequence of DS-SS Signals Based on Improved Propagator Method[J]. Acta Electronica Sinica, 2020, 48(1): 9-16. DOI： 10.3969/j.issn.0372-2112.2020.01.002.

摘要

本文研究了DS-SS信号的扩频序列盲估计问题，针对现有特征值分解算法失步时间估计不准和计算量大等缺点，提出一种基于改进传播算子的扩频序列盲估计算法，该算法对传统传播算子估计信号子空间的方法进行了改进，使之能够估计扩频序列，并利用信号子空间估计信号失步时间.与基于特征值分解的方法相比，本文算法提高了失步时间的估计精度，并有效的降低了计算量.最后对算法计算量和性能进行了仿真分析和验证，结果表明本文算法有效，计算量远小于现有特征值分解算法，且估计性能在低信噪比条件下更优.

Abstract

Aiming at the problem of inaccurate estimation of out-of-step time and large computation in eigenvalue decomposition algorithm

the direct sequence spread spectrum (DS-SS) signal blind estimation problem of spread spectrum sequence is studied

and an algorithm based on improved propagator is proposed in this paper. The algorithm can estimate the spread spectrum sequence and the signal out-of-step time by using the signal subspace. Compared with the eigenvalue decomposition method

the estimation accuracy of the out-of-step time is improved and the amount of calculation is effectively reduced. Finally

the computational amount and performance of the algorithm are simulated and verified. The results show that the proposed algorithm is effective

the computational amount is far less than the eigenvalue decomposition algorithm

and the performance is better under the condition of low SNR.

关键词

Keywords

references

Views

360

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Blind Reduced-Rank Multi-User Detector for DS-CDMA Systems Based on Signal Subspace

Modified Mahalanobis Distance by Compensation for Errors of Non-dominant Eigenvalues and Asymmetrical Distribution

Iteration Projection Reconstruction Method for 3D Non-Rigid Based on Minimal Eigenvalue

Constuction and Analyysis of a Class Non-linear Spread Spectrum Sequence

Related Author

YAN Yu-cai

DONG En-qing

SHI Peng-fei

LI Guo-hong

GU Hai-jun

ZHAO Xiao-hui

OU Shi-feng

LIU Shi-gang

Related Institution

School of Information Engineering,Shandong University at Weihai

Institute of Image Processing and Pattern Recognition,Shanghai JiaoTong University

Laboratory of Information Science,College of Communication Engineering,Jilin University

Key Laboratory of Modern Teaching Technology, Ministry of Education

College of Information Engineering Henan University of Science and Technology Luoyang Henan China

⁰