Estimation of Signal-to-Noise Ratio Based on Feature Selection and Weight Optimization of High-Order Moments

LI Tao; SU Nan; WEI Di-shan; LI Yong-zhao; ZHU Ruo-nan; ZHAO Xin-yu; ZHOU Shuai

doi:10.12263/DZXB.20230993

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Estimation of Signal-to-Noise Ratio Based on Feature Selection and Weight Optimization of High-Order Moments

PAPERS | 更新时间：2025-12-11

- Estimation of Signal-to-Noise Ratio Based on Feature Selection and Weight Optimization of High-Order Moments
- ACTA ELECTRONICA SINICA Vol. 52, Issue 12, Pages: 3976-3984(2024)
- 作者机构：
  
  1.西安电子科技大学广州研究院，广东广州 510000
  2.西安电子科技大学通信工程学院，陕西西安 710071
  3.北京跟踪与通信技术研究所，北京 100094
- 作者简介：
- 基金信息：
  
  Guangzhou Basic and Applied Basic Research Foundation(2023A04J1740)
- DOI：10.12263/DZXB.20230993
  CLC： TN911.6;TN911.7
- Received：20 October 2023，
  
  Revised：2024-07-11，
  
  Published：25 December 2024
- 稿件说明：
移动端阅览
李涛, 苏楠, 韦荻山, 等. 基于高阶矩特征选择和权值优化的信噪比估计[J]. 电子学报, 2024, 52(12): 3976-3984.

LI Tao, SU Nan, WEI Di-shan, et al. Estimation of Signal-to-Noise Ratio Based on Feature Selection and Weight Optimization of High-Order Moments[J]. Acta Electronica Sinica, 2024, 52(12): 3976-3984.
李涛, 苏楠, 韦荻山, 等. 基于高阶矩特征选择和权值优化的信噪比估计[J]. 电子学报, 2024, 52(12): 3976-3984. DOI：10.12263/DZXB.20230993

LI Tao, SU Nan, WEI Di-shan, et al. Estimation of Signal-to-Noise Ratio Based on Feature Selection and Weight Optimization of High-Order Moments[J]. Acta Electronica Sinica, 2024, 52(12): 3976-3984. DOI：10.12263/DZXB.20230993

摘要

信噪比估计作为信号参数估计的一个重要组成部分，可为功率控制、调制方式识别、信道估计以及动态模式切换等技术提供先验信息.目前基于高阶矩的信噪比估计由于其计算复杂度低、实时性高的优势一直受到众多学者的关注，然而基于高阶矩的估计存在低信噪比和高信噪比两个极端情况下估计性能变差的缺陷.本文在分析信号高阶矩分布特性的基础上，设计了一种基于高阶矩特征选择和线性组合的信噪比估计算法.首先通过分析不同矩统计量与信噪比值之间的关系，对高阶矩特征进行筛选.在此基础上，对选择的高阶矩特征进行线性组合，并设计优化算法求解线性组合的权值系数.仿真结果表明，本文所提信噪比估计方法对低信噪比和高信噪比下的估计性能做了折衷，对比已有基于高阶矩的信噪比估计算法，在-10~20 dB范围内能够较好地兼顾低信噪比和高信噪比的估计性能.

Abstract

As an important part of signal parameter estimation

signal-to-noise ratio (SNR) estimation can provide prior information for power control

modulation classification

channel estimation

and dynamic mode switching

etc. Recently

high-order moments (HOMs) based algorithms have been widely concerned due to the advantages of low computational complexity and high real-time property. However

the estimation performance of the HOMs-based algorithms is still constrained in extremely low or high SNR regions. In this paper

a SNR estimation algorithm based on feature selection and linear combination of HOMs is designed

according to the distribution characteristics of HOMs. Firstly

the HOMs are screened by analyzing the relationship between different moments and SNR values. Based on this

we resort to the linear combination of the selected HOMs to estimate SNR. And the weights of linear combination are calculated by designing an optimization problem. The simulation results show that the proposed SNR estimation scheme makes a tradeoff among the estimation performance of high and low SNR regions. Compared with the existing HOMs-based algorithms

the proposed algorithm has a more comprehensive performance in the range of -10 dB to 20dB.

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references

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