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Volume 41 Issue 4
Mar.  2019
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Tianqi ZHANG, Donghua LIU, Shuai YUAN, Sheng WANG. Blind Estimation of the Pseudo Code Period and Combination Code Sequence for Composite Binary Offset Carrier Signal[J]. Journal of Electronics & Information Technology, 2019, 41(4): 917-924. doi: 10.11999/JEIT180444
Citation: Tianqi ZHANG, Donghua LIU, Shuai YUAN, Sheng WANG. Blind Estimation of the Pseudo Code Period and Combination Code Sequence for Composite Binary Offset Carrier Signal[J]. Journal of Electronics & Information Technology, 2019, 41(4): 917-924. doi: 10.11999/JEIT180444

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

doi: 10.11999/JEIT180444
Funds:  The National Natural Science Foundation of China (61671095, 61371164, 61702065, 61701067, 61771085), The Project of Key Laboratory of Signal and Information Processing of Chongqing (CSTC2009CA2003), The Chongqing Graduate Research and Innovation Project (CYS17219), The Research Project of Chongqing Educational Commission (KJ1600427, KJ1600429)
  • Received Date: 2018-05-10
  • Rev Recd Date: 2018-10-11
  • Available Online: 2018-11-02
  • Publish Date: 2019-04-01
  • 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.

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