基于聚类的软扩频信号盲解扩方法

该文研究的是在未知扩频序列的情况下，实现多序列直扩信号(也称软扩频信号)的解扩。对于传统直扩信号来说，主模解扩法(DMDS)被证明是一种有效的盲解扩方法，但是它并不适用于软扩频信号的盲解扩问题。借鉴无监督聚类分析的思想，该文提出了一种基于聚类的软扩频信号盲解扩方法(KCDS)。该方法将软扩频信号分成不重叠的信号向量，利用这些向量的聚类特征完成扩频码的估计，通过最大化平均侧影宽度完成延迟时间和扩频码数量的估计。计算机仿真表明，在零均值噪声环境下，KCDS算法可以解决软扩频信号的盲解扩问题。

Clustering Based Blind Despread Method of Tamed Direct Sequence Spread Spectrum Signals

Blind despread of multi-sequence Direct Sequence Spread Spectrum signals (tamed DSSS signals) with unknown spreading codes are discussed in this paper. The Dominant Mode DeSpreading (DMDS) algorithm is certified to be a successful solution for the blind estimation of the conventional DSSS signals. However, it proved to be not applicable to tamed DSSS signals. Borrowing unsupervised cluster analysis ideas, a novel method named K-means Clustering DeSpreading (KCDS) algorithm for tamed DSSS signals is proposed. KCDS algorithm, divides the tamed DSSS signal into non-overlapped individuals, and then exploits the clustering property of these individuals to estimate the spreading codes. The delay time and the number of spreading codes can be estimated by maximizing the average silhouette width. It is demonstrated to be effective via simulation results for a 32-ary DSSS signal in the presence of zero-mean noise.