New Adaptive Recursive M-Estimation Algorithm and Its Performance Analysis

HU Mou-fa; SHEN Yan; CHEN Zeng-ping

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New Adaptive Recursive M-Estimation Algorithm and Its Performance Analysis

更新时间：2025-07-16

- New Adaptive Recursive M-Estimation Algorithm and Its Performance Analysis
- Acta Electronica Sinica Vol. 35, Issue 9, Pages: 1651-1655(2007)
- 作者机构：
  
  1. 国防科学技术大学ATR重点实验室,湖南,长沙,410073
  2. 国防科技大学电子科学与工程学院,湖南,长沙,410073
  3. 国防科学技术大学ATR重点实验室湖南长沙,410073
  4. 国防科技大学电子科学与工程学院湖南长沙,410073
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN911.73
- Published：2007
- 稿件说明：
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HU Mou-fa, SHEN Yan, CHEN Zeng-ping. New Adaptive Recursive M-Estimation Algorithm and Its Performance Analysis[J]. Acta Electronica Sinica, 2007, 35(9): 1651-1655.
DOI：

HU Mou-fa, SHEN Yan, CHEN Zeng-ping. New Adaptive Recursive M-Estimation Algorithm and Its Performance Analysis[J]. Acta Electronica Sinica, 2007, 35(9): 1651-1655. DOI：

摘要

针对复杂噪声环境下的参数估计问题

提出了一种稳健的自适应序贯M估计算法(Adaptive Recursive M-Estimation

ARME)

并从理论分析和Monte Carlo实验仿真两方面分析了该算法的收敛性、渐进无偏特性和稳健性.理论分析和仿真试验表明:在高斯白噪声背景下

ARME具有与序贯最小二乘算法(Recursive Least Square

RLS)相近的性能;在有突出干扰等非高斯噪声背景下

与RLS相比

ARME的参数估计收敛速度更快

估计误差更小

而且在稳健性上大大优于RLS.

Abstract

To estimate the parameters in complex noise situation effectively

a new robustness algorithm for adaptive signal process is presented named adaptive recursive M-estimation (ARME).And its properties of convergence

asymptotic unbiasedness and robustness are analyzed from theory and Monte Carlo simulations.Results and simulations show that the ARME algorithm obtains the advantages of the recursive least square (RLS) in Gaussian white noise (WGN).More importantly

in the no-Gaussian noise situation such as WGN with high interference

they show that the ARME algorithm has a better convergence rate and a less estimation deviation than those of the RLS.Besides that

the ARME is more robustness compared with the RLS.

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Related Institution

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