The Combining Algorithm of BPSK Signal in Antenna Arraying Based on Cyclic Correlations

KONG De-qing; LI Chun-lai; SHI Hu-li

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The Combining Algorithm of BPSK Signal in Antenna Arraying Based on Cyclic Correlations

更新时间：2025-07-16

- The Combining Algorithm of BPSK Signal in Antenna Arraying Based on Cyclic Correlations
- Acta Electronica Sinica Vol. 39, Issue 9, Pages: 2080-2085(2011)
- 作者机构：
  
  中国科学院国家天文台,北京,100012
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN820.1
- Published：2011
- 稿件说明：
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KONG De-qing, LI Chun-lai, SHI Hu-li. The Combining Algorithm of BPSK Signal in Antenna Arraying Based on Cyclic Correlations[J]. Acta Electronica Sinica, 2011, 39(9): 2080-2085.
DOI：

KONG De-qing, LI Chun-lai, SHI Hu-li. The Combining Algorithm of BPSK Signal in Antenna Arraying Based on Cyclic Correlations[J]. Acta Electronica Sinica, 2011, 39(9): 2080-2085. DOI：

摘要

深空网天线组阵中的干扰信号会严重影响信号的合成性能.针对全频谱合成方式下的SIMPLE合成算法

推导了干扰相关时合成信号的信噪比计算公式.分析了基于循环互相关的载波相位差估计方法

提出了BPSK信号循环自相关的相位差估计方法

并首次将两种方法应用到了天线组阵信号合成技术中.理论分析和仿真实验表明

在不增加额外计算量的基础上

循环互相关和自相关都具有较高的相位差估计精度和抗干扰能力.循环自相关比循环互相关具有更好的低信噪比相位差估计性能和信号合成性能

以及更低的天线间数据传输速率;而循环互相关具有更广的循环频率选择范围.

Abstract

The arraying combining performance in deep space network is seriously affected by interference signals.The SNR formula of the combining signal with interference signals is derived.The phase residual estimate algorithm is analyzed by use of the cyclic cross-correlation

and another estimate algorithm of BPSK signals is presented based on the cyclic auto-correlation.The two methods are firstly used in the arraying signal combining.Theoretical analysis and simulation results show that both the two methods have highly phase difference estimation accuracy and interference suppression ability

without additional computation complexity.Compared with the cyclic cross-correlation

the cyclic auto-correlation has higher estimation accuracy and combining performance under conditions of low SNR

and has lower exchange data rate between the antennas.However

there are more cyclic frequency options for cyclic cross-correlation.

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