التفاصيل البيبلوغرافية
| العنوان: |
基于 SVD-K-means 算法的软扩频信号伪码 序列盲估计. (Chinese) |
| Alternate Title: |
Blind estimation of pseudo-code sequence of soft spread spectrum signal based on SVD-K-means algorithm. (English) |
| المؤلفون: |
张慧芝, 张天骐, 方 蓉, 罗庆予 |
| المصدر: |
Systems Engineering & Electronics; Jan2024, Vol. 46 Issue 1, p326-333, 8p |
| مصطلحات موضوعية: |
SINGULAR value decomposition, SIGNALS & signaling |
| Abstract (English): |
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. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
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. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
