组合二进制偏移载波信号的伪码周期及组合码序列盲估计

针对在非协作通信以及低信噪比下组合二进制偏移载波(CBOC)信号伪码周期和组合码序列较难估计的问题，该文提出了2次谱算法与基于径向基函数(RBF)神经网络算法。对输入信号进行2次功率谱计算，可以得到CBOC信号的伪码周期。在此基础上，首先对接收的1周期组合码序列进行重叠分段，其次优化筛选出学习系数，对每段数据向量作为RBF神经网络的输入信号并进行有监督地调节，最后对每段数据向量多次输入并反复训练权值向量就可以恢复原组合码序列。仿真结果表明，利用2次谱可以在低信噪比下估计出伪码周期；在误码率低于1%的情况下，所提出的RBF神经网络相比于反向传播(BP)神经网络与Sanger神经网络，信噪比分别提高1 dB和3 dB，并且在同等条件下所需的数据组数较少。

Blind Estimation of the Pseudo Code Period and Combination Code Sequence for Composite Binary Offset Carrier Signal

For the problems of the Composite Binary Offset Carrier (CBOC) signal pseudo code period and combination code sequence are difficult to estimate in a non-cooperative context, two blind methods are proposed based on power spectrum reprocessing and Radial Basis Function (RBF) neural networks. It can get the CBOC pseudo code period through two power spectrum calculations. Firstly, the received one pseudo code period is overlapped segmentation based on the estimated pseudo code period. Secondly, the learning coefficient is optimized selection and each segment of date vector as an input signal to the RBF neural networks to supervised adjustment. Finally, through the continuous input signal, it can restore the original combination code sequence according to the convergent weight vectors. Simulation results show that the pseudo code period can be estimated using the secondary power spectrum under low Signal-to-Noise Ratio (SNR). Compared with the Back Propagation (BP) neural networks and the Sanger neural networks, the proposed RBF neural networks improve the SNR by 1 dB and 3 dB respectively and the number of data groups required is less through RBF neural networks under the same condition.