基于平均内积和相关判决函数的DSSS信号伪码序列盲估计

Blind estimation of DSSS pseudo-random sequence based on average inner product and correlative decision function

针对已有扩频码估计方法计算复杂度高的问题,提出一种基于平均内积和相关判决函数的扩频码估计方法.首先采用窗长为伪码周期的滑动窗函数将信号划分为不重叠数据段,以各分段数据为行向量依次排列构造数据矩阵,并根据不同起始点条件下分段后所对应矩阵平均内积的最大值确定伪码同步起始点;然后在实现伪码同步的基础上,利用相关判决函数估计出伪码序列各码元与第1个码元之间的符号关系,从而实现伪码序列的盲估计;最后通过理论推导说明所提方法的有效性,并在相同的实验条件下分别给出该方法与基于特征分解、三阶相关的常见方法在扩频码估计的性能和计算量上的仿真对比.实验数据表明,在SNR?3时,所提出的相关判决函数法可以得到准确的估计,且计算量相比特征分解、三阶相关法也大大降低了.

A new method based on average correlation and correlation decision function(CDF) is proposed to solve the problem of large computational complexity, for estimating the spreading sequence of direct sequence spread spectrum(DSSS) in this paper. Firstly, the signal is divided into non-overlapping data segments by using the sliding window function with the window length equal tothe pseudo noise(PN) code period. The data matrix is arranged in the order of each segment data as one row vector. Then, the maximum value of the average inner product of the corresponding matrices, which is obtained according to the different starting point conditions, can be used to determine the PN code synchronization starting point. On the basis of the realization of PN code synchronization, the symbolic relation between each symbol of the PN sequence and the first symbol is estimated by using the correlative decision function, so as to realize the blind estimation of the pseudo sequence. The effectiveness of the proposed method is showed by theoretical derivations. The performance and the computational complexity are compared between the proposed method and the common methods based on the eigenvalue decomposition(EVD) and triple correlation function(TCF) through simulation comparison experiments. It turns out that, when SNR ? 3, using the CDF method proposed in this paper can get an accurate estimate, and the computational complexity is greatly reduced compared with methods based on the EVD and TCF.