Noise Energy Estimator Based on Sparseness of Time-Frequency Domain for Broadband Frequency-Hopping Signal

XIN Ji-rong; LU Lu-xi; BAO Xin; CHENG Jian

doi:10.3969/j.issn.0372-2112.2014.10.010

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Noise Energy Estimator Based on Sparseness of Time-Frequency Domain for Broadband Frequency-Hopping Signal

更新时间：2025-07-16

- Noise Energy Estimator Based on Sparseness of Time-Frequency Domain for Broadband Frequency-Hopping Signal
- Acta Electronica Sinica Vol. 42, Issue 10, Pages: 1932-1937(2014)
- 作者机构：
  
  1. 国防科学技术大学电子科学与工程学院,湖南,长沙,410073
  2. 盲信号处理重点实验室,四川,成都,610041
  3. 国防科学技术大学电子科学与工程学院,湖南,长沙,410073
  4. 盲信号处理重点实验室,四川,成都,610041
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2014.10.010
  CLC： TN911.23
- Published：2014
- 稿件说明：
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XIN Ji-rong, LU Lu-xi, BAO Xin, et al. Noise Energy Estimator Based on Sparseness of Time-Frequency Domain for Broadband Frequency-Hopping Signal[J]. Acta Electronica Sinica, 2014, 42(10): 1932-1937.
DOI：

XIN Ji-rong, LU Lu-xi, BAO Xin, et al. Noise Energy Estimator Based on Sparseness of Time-Frequency Domain for Broadband Frequency-Hopping Signal[J]. Acta Electronica Sinica, 2014, 42(10): 1932-1937. DOI： 10.3969/j.issn.0372-2112.2014.10.010.

摘要

背景噪声估计是自适应盲信号检测的依据.针对传统背景噪声估计复杂度高不适用于宽带卫星跳频信号盲处理系统的现实问题

提出了一种利用跳频信号稀疏性的背景噪声估计算法.理论分析了该算法的可靠性和复杂度

并应用仿真数据和实际信号验证了算法的有效性.相比传统算法

该算法可提供相近的噪声估计结果

而计算复杂度和数据缓存大为降低.

Abstract

Noise estimation is the basis of the adaptive blind signal detection.It provides the thresholds of automatic detector based on the CFAR (Constant False Alarm Rate) rule.The traditional noise estimator was complicated and cannot be easily implemented in the on-line blind signal processing system for the satellite broadband frequency-hopping signal.Utilizing the sparseness of the frequency-hopping signal

a new algorithm was proposed to facilitate the noise estimation.The reliability and complexity of the algorithm was analyzed theoretically

and the effectiveness was verified through both simulations and real signal experiments.Compared to the classical algorithm

the proposed algorithm can provide similar noise estimation results

while its computational complexity and buffer consumption was significantly reduced.

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