一种针对噪声不确定性的顽健循环特征检测法

陈强; 金明; 童景文

doi:10.11959/j.issn.1000-0801.2016211

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一种针对噪声不确定性的顽健循环特征检测法

研究与开发 | 更新时间：2024-06-05

- 一种针对噪声不确定性的顽健循环特征检测法
- A robust cyclic-feature detection against noise uncertainty
- 电信科学 2016年32卷第10期页码：71-76
- 作者机构：
- 作者简介：
  
  [ "陈强（1991-），男，宁波大学信息科学与工程学院硕士生，主要研究方向为认知无线电中的频谱感知。" ]
  [ "金明（1981-），男，博士，宁波大学信息科学与工程学院副教授，主要研究方向为认知无线电技术、阵列信号处理等。" ]
  [ "童景文（1991-），男，宁波大学信息科学与工程学院硕士生，主要研究方向为认知无线电中的频谱感知。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61301152);国家自然科学基金资助项目;The National Natural Science Foundation of China(61571250);宁波市自然科学基金资助项目;Ningbo Natural Science Foundation of China(2016A610058);浙江省信息与通信工程重中之重学科开放基金资助项目;Zhejiang Open Foundation of the Most Important Subjects of Information and Communication Engineering(xkx11402)
- DOI：10.11959/j.issn.1000-0801.2016211
  中图分类号： TN911
- 网络出版日期：2016-10，
  
  纸质出版日期：2016-10-20
- 稿件说明：
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陈强, 金明, 童景文. 一种针对噪声不确定性的顽健循环特征检测法[J]. 电信科学, 2016,32(10):71-76.

Qiang CHEN, Ming JIN, Jingwen TONG. A robust cyclic-feature detection against noise uncertainty[J]. Telecommunications science, 2016, 32(10): 71-76.
陈强, 金明, 童景文. 一种针对噪声不确定性的顽健循环特征检测法[J]. 电信科学, 2016,32(10):71-76. DOI： 10.11959/j.issn.1000-0801.2016211.

Qiang CHEN, Ming JIN, Jingwen TONG. A robust cyclic-feature detection against noise uncertainty[J]. Telecommunications science, 2016, 32(10): 71-76. DOI： 10.11959/j.issn.1000-0801.2016211.

摘要

循环特征检测法的检测性能远优于能量检测法，但是噪声不确定性会使这一优势变得不明显。针对这一问题，提出了一种能够对抗噪声不确定性的循环特征检测法。推导证明了在二维循环谱中，不同循环频率上的噪声分量具有独立同分布的特性。基于这一特性，利用授权用户信号循环谱峰以外的值来估计循环谱峰处的噪声分布，并获得不需要噪声功率先验知识的判决门限，从而避免了噪声不确定性的影响。实验结果表明，所提方法的检测性能接近噪声功率精确已知的循环特征检测法。

Abstract

The detection performance of cyclic-feature detection is much better than energy detection. However

noise uncertainty will degrade its performance severely. Aiming at this problem

a cyclic-feature detection method resisting to noise uncertainty was proposed. It was proved that the noise cyclic spectrum components at different cycle frequencies were independent and identically distributed in two-dimensional cyclic spectrum. Based on this

the noise distribution at the cycle peak was estimated using all values except the spectrum peak positions of licensed user signal

and decision threshold was obtained without noise power prior knowledge

so as to avoid the influence of noise uncertainties. Simulation results demonstrate that the performance of the proposed method is close to that of cyclic-feature detection method with known noise power.

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references

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