A Low Complexity Acquisition Algorithm for DSSS Signal with Low SNR and Non-Coherent Data Modulation

XIONG Zhu-lin; AN Jian-ping

doi:10.3969/j.issn.0372-2112.2016.04.001

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A Low Complexity Acquisition Algorithm for DSSS Signal with Low SNR and Non-Coherent Data Modulation

更新时间：2025-07-16

- A Low Complexity Acquisition Algorithm for DSSS Signal with Low SNR and Non-Coherent Data Modulation
- Acta Electronica Sinica Vol. 44, Issue 4, Pages: 753-760(2016)
- 作者机构：
  
  北京理工大学信息与电子学院,北京,100081
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2016.04.001
  CLC： TN914.42
- Published Online：25 April 2016，
  
  Published：2016
- 稿件说明：
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XIONG Zhu-lin, AN Jian-ping. A Low Complexity Acquisition Algorithm for DSSS Signal with Low SNR and Non-Coherent Data Modulation[J]. Acta Electronica Sinica, 2016, 44(4): 753-760.
DOI：

XIONG Zhu-lin, AN Jian-ping. A Low Complexity Acquisition Algorithm for DSSS Signal with Low SNR and Non-Coherent Data Modulation[J]. Acta Electronica Sinica, 2016, 44(4): 753-760. DOI： 10.3969/j.issn.0372-2112.2016.04.001.

摘要

针对低信噪比非相干直扩信号捕获问题

本文提出一种区域并行块捕获算法.该算法首先对调制数据偏移量所在区域进行并行预估计并舍弃可能含有数据跳变的非有效数据段

然后对各支路有效数据段进行快速傅里叶变换和非相干累加得到各频点的累加检测值

最后对累加检测峰值进行恒虚警判定和抛物插值以实现信号检测和参数的精确估计.理论分析和仿真结果均表明

本文算法能够根据虚警漏警概率、捕获时间和信噪比要求对参数进行优化

在实现快速可靠捕获的同时保持较低的计算复杂度和资源消耗.

Abstract

A block acquisition algorithm based on parallel interval estimation is proposed for direct sequence spread spectrum (DSSS) signal with low signal-to-noise ratio (SNR) and non-coherent data modulation.First

the existence interval of data modulation offset is estimated in parallel with invalid data section abandoned against possible bit-flip.Then fast Fourier transform (FFT) and incoherent accumulation are adopted in the valid data sections of each estimation branch to acquire cumulative detection value of each frequency point.Finally

the constant false alarm rate detection and parabolic interpolation are performed on the maximum cumulative detection value to fulfill signal detection and parameter estimation.Both analysis and simulation demonstrate that by adjusting key parameters to match the requirement of false alarm and detection probability

acquisition time and SNR

the proposed algorithm achieves good performance with low computational complexity and resource consumption.

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