基于特征分解的直扩信号盲估计研究与仿真

Eigenvector Decomposition Based DSSS Signal Blind Estimation and Simulation

对含噪声直接序列扩频(Direct Spread Spectrum Sequence,DSSS)信号以2倍信息码元2T0时间窗截断计算自相关矩阵并做特征分解,利用最大特征向量对接收DSSS信号的PN码进行盲检测恢复,对最大特征向量求取PN码长累加和、寻找最大值的方法计算PN码在特征向量中的位置。仿真分析表明,该方法能够在低信噪比下对伪随机序列进行估计,同时得到了接收信号中PN码的初始相位。

To direct spread spectrum sequence （ DSSS） signals with noise, cut off received signal with two times of information code element period （i. e. 2T0 time window） to calculate auto-correlation matrix and perform eigen-vector decomposition;conduct blind detection restore to PN code of received DSSS signal by using maximum eigen-vector;calculate PN code length accumulation sum of maximum eigenvector;and calculate location of the PN code in eigenvector by way of seeking maximum value method. Simulation analysis indicates that pseudo random se-quence can be estimated under low signal-to-noise ratio, meanwhile initial phase of PN code in received signal can be obtained.