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

Abstract

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

CLC Number:

TN911.7

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

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