基于近似核密度估计的近场多声源定位算法

房玉琢; 许志勇; 赵兆

doi:10.11959/j.issn.1000-436x.2017013

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基于近似核密度估计的近场多声源定位算法

学术论文 | 更新时间：2024-06-05

- 基于近似核密度估计的近场多声源定位算法
- Near-field localization algorithm of multiple sound sources based on approximated kernel density estimator
- 通信学报 2017年38卷第1期页码：106-116
- 作者机构：
  
  南京理工大学电子工程与光电技术学院，江苏南京 210094
- 作者简介：
  
  [ "房玉琢（1987-），男，江苏南京人，南京理工大学博士生，主要研究方向为阵列信号处理、声学探测、盲信道辨识等。" ]
  [ "许志勇（1968-），男，江苏南京人，博士，南京理工大学副教授，主要研究方向为阵列信号处理、声学探测、雷达技术等。" ]
  [ "赵兆（1979-），男，湖北襄阳人，博士，南京理工大学副教授，主要研究方向为声探测系统与信号处理、时频分析。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61171167);国家自然科学基金资助项目(61401203);江苏省自然科学基金资助项目(BK20130776)
- DOI：10.11959/j.issn.1000-436x.2017013
  中图分类号： TN911.72
- 网络出版日期：2017-01，
  
  纸质出版日期：2017-01-25
- 稿件说明：
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房玉琢, 许志勇, 赵兆. 基于近似核密度估计的近场多声源定位算法[J]. 通信学报, 2017,38(1):106-116.

Yu-zhuo FANG, Zhi-yong XU, Zhao ZHAO. Near-field localization algorithm of multiple sound sources based on approximated kernel density estimator[J]. Journal on communications, 2017, 38(1): 106-116.
房玉琢, 许志勇, 赵兆. 基于近似核密度估计的近场多声源定位算法[J]. 通信学报, 2017,38(1):106-116. DOI： 10.11959/j.issn.1000-436x.2017013.

Yu-zhuo FANG, Zhi-yong XU, Zhao ZHAO. Near-field localization algorithm of multiple sound sources based on approximated kernel density estimator[J]. Journal on communications, 2017, 38(1): 106-116. DOI： 10.11959/j.issn.1000-436x.2017013.

摘要

针对混响环境下的近场多声源定位问题，提出了一种基于近似核密度估计（KDE）的算法模型。引入多阶段（MS）分频带处理有效解决宽间距时的空域模糊，同时，构建空域似然率函数（SLF）通过相加（S）及相乘（P）2种算子进行多维融合，从而衍生出S-KDE、P-KDE、S-KDEMS和P-KDEMS 4种算法。通过对均方根误差（RMSE）以及表征辨识度的SLF百分比（PSLF）这2个统计指标的综合比较，证实了P-KDEMS是一种具有较高稳健性与辨识度的近场多声源定位算法。

Abstract

For near-field localization of multiple sound sources in reverberant environments

a algorithm model based on approximated kernel density estimator (KDE) was proposed.Multi-stage (MS) of sub-band processing was introduced to effectively solve the spatial aliasing by wide spacing.Spatial likelihood function (SLF) was built for multi-dimensional fusion by using two operators

sum (S) and prod (P).Then four algorithms

S-KDE

P-KDE

S-KDEMS

P-KDEMS

were derived.By the comprehensive comparison of the two statistical indicators root mean square error (RMSE) and percentage of SLF (PSLF) which denoted the recognition

P-KDEMS is confirmed as a near-field localization algorithm of multiple sound sources with high robustness and recognition.

关键词

Keywords

references

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