基于SVD-K-means算法的软扩频信号伪码序列盲估计

doi:10.12305/j.issn.1001-506X.2024.01.37

系统工程与电子技术 ›› 2023, Vol. 46 ›› Issue (1): 326-333.doi: 10.12305/j.issn.1001-506X.2024.01.37

• 通信与网络 • 上一篇

基于SVD-K-means算法的软扩频信号伪码序列盲估计

张慧芝, 张天骐, 方蓉, 罗庆予

重庆邮电大学通信与信息工程学院, 重庆 400065

收稿日期:2022-12-20 出版日期:2023-12-28 发布日期:2024-01-11
通讯作者: 张慧芝
作者简介:张慧芝(2000—), 女, 硕士研究生, 主要研究方向为扩频信号盲处理
张天骐(1971—), 男, 教授, 博士研究生导师, 主要研究方向为通信信号的调制解调、盲处理、语音信号处理、神经网络实现以及FPGA、VLSL实现
方蓉(1999—), 女, 硕士研究生, 主要研究方向为卫星扩频信号捕获
罗庆予(1999—), 女, 硕士研究生, 主要研究方向为语音信号处理、语音增强
基金资助:
国家自然科学基金(61671095);国家自然科学基金(61702065);国家自然科学基金(61701067);国家自然科学基金(61771085);信号与信号处理重庆市市级重点实验室建设项目(CSTC2009CA2003);重庆市自然基金(cstc2021jcyj-msxmX0836);重庆市教育委员会科研项目(KJ1600427);重庆市教育委员会科研项目(KJ1600429)

Blind estimation of pseudo-code sequence of soft spread spectrum signal based on SVD-K-means algorithm

Huizhi ZHANG, Tianqi ZHANG, Rong FANG, Qingyu LUO

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2022-12-20 Online:2023-12-28 Published:2024-01-11
Contact: Huizhi ZHANG

摘要/Abstract

摘要：

针对通信中软扩频信号伪码序列盲估计困难的问题, 提出一种奇异值分解(singular value decomposition, SVD)和K-means聚类相结合的方法。该方法先对接收信号按照一倍伪码周期进行不重叠分段构造数据矩阵。其次对数据矩阵和相似性矩阵分别进行SVD完成对伪码序列集合规模数的估计、数据降噪、粗分类以及初始聚类中心的选取。最后通过K-means算法优化分类结果, 得到伪码序列的估计值。该算法在聚类之前事先确定聚类数目, 大大减少了迭代次数。同时实验结果表明, 该算法在信息码元分组小于5 bit, 信噪比大于-10 dB时可以准确估计出软扩频信号的伪码序列, 性能较同类算法有所提升。

关键词: 软扩频信号, 盲估计, 奇异值分解, K-means

Abstract:

Aiming at the difficulty of blind estimation of pseudo-code sequence of soft spread spectrum signal in communication, a method of singular value decomposition (SVD) and K-means clustering was proposed. In this method, the data matrix of the received signal is constructed by non-overlapping segments according to the length of a periodic pseudo-code sequence. Secondly, the data matrix and the similarity matrix are respectively evaluated by SVD to complete the estimation of the size of the pseudo-code set, data noise reduction, rough classification and the selection of the initial clustering center. Finally, K-means algorithm is used to optimize the classification results, and obtain the estimated value of the pseudo code sequence. The algorithm determines the number of clusters before clustering, which greatly reduces the number of iterations. At the same time, the experimental results show that the algorithm can accurately estimate the pseudo-code sequence of the soft spread spectrum signal when the packet of information symbols is less than 5 bit and the signal to noise ratio (SNR) is greater than -10 dB, and the performance is improved compared with other algorithms.

Key words: soft spread spectrum signal, blind estimation, singular value decomposition (SVD), K-means

中图分类号:

TN911.7

张慧芝, 张天骐, 方蓉, 罗庆予. 基于SVD-K-means算法的软扩频信号伪码序列盲估计[J]. 系统工程与电子技术, 2023, 46(1): 326-333.

Huizhi ZHANG, Tianqi ZHANG, Rong FANG, Qingyu LUO. Blind estimation of pseudo-code sequence of soft spread spectrum signal based on SVD-K-means algorithm[J]. Systems Engineering and Electronics, 2023, 46(1): 326-333.

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基于SVD-K-means算法的软扩频信号伪码序列盲估计

Blind estimation of pseudo-code sequence of soft spread spectrum signal based on SVD-K-means algorithm

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