Research on multi-target recognition method based on WSN and blind source separation

Pengju HE; Gangyi LIU; Siyi LIU

doi:10.11959/j.issn.2096-3750.2019.00095

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Research on multi-target recognition method based on WSN and blind source separation

Theory and Technology | 更新时间：2024-06-05

- Research on multi-target recognition method based on WSN and blind source separation
- Chinese Journal on Internet of Things Vol. 3, Issue 1, Pages: 45-50(2019)
- 作者机构：
  
  1. 西北工业大学深圳研究院，广东深圳 518057
  2. 西北工业大学自动化学院，陕西西安 710072
  3. 常州和仕达电子科技有限公司，江苏常州 213022
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2019.00095
  CLC： TP393
- Published：30 March 2019，
  
  Published Online：2019-03，
- 稿件说明：
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PENGJU HE, GANGYI LIU, SIYI LIU. Research on multi-target recognition method based on WSN and blind source separation. [J]. Chinese journal on internet of things, 2019, 3(1): 45-50.
DOI：

PENGJU HE, GANGYI LIU, SIYI LIU. Research on multi-target recognition method based on WSN and blind source separation. [J]. Chinese journal on internet of things, 2019, 3(1): 45-50. DOI： 10.11959/j.issn.2096-3750.2019.00095.

摘要

针对利用无线传感器网络进行多目标检测识别中存在的信号混叠问题，提出一种可以确定目标个数的盲源分离算法对混叠信号进行分离，得到准确的源信号。该算法以多路混合观测信号为研究对象，提出采用基于特征值方法确定多路混合信号中独立信源的个数，运用基于非负矩阵分解的盲源分离算法得到分离信号。实验结果表明，本文所提算法能够确定目标个数、得到正确的分离信号，可用于解决多目标检测识别中的信号混叠问题。

Abstract

Aiming at the problem of signal aliasing in multi-target detection and recognition using wireless sensor network (WSN)

a blind source separation algorithm was proposed

which can determine the number of targets and obtain the accurate source signals.In this algorithm

the multichannel mixed signal was used as the analysis object

the number of source signals was determined based on the eigenvalue method and then the blind source separation algorithm based on the non-negative matrix factorization was used to obtain the separation signals.The experimental results indicate that the number of targets can be determined and the accurate separation signals can be obtained by the proposed scheme.It can be applied to solve the problem of signal aliasing in multi-target detection and recognition.

关键词

无线传感器网络多目标识别盲源分离非负矩阵分解

Keywords

wireless sensor network (WSN)multi-target recognitionblind source separation (BSS)

references

吴瑞睿, 刘洁琳 . 无线传感器网络综述[J]. 科技创新与应用, 2018,234(14): 71-72.

WU R R, LIU J L . Survey of wireless sensor networks[J]. Technology Innovation and Application, 2018,234(14): 71-72.

刘桂林, 孔祥维, 刘航 . 基于无线传感器网络的车辆检测识别算法研究[J]. 传感器与微系统, 2010,29(2): 9-12.

LIU G L, KONG X W, LIU H . Research on vehicle detection and classification algorithm based on wireless sensor networks[J]. Transducer and Microsystem Technologies, 2010,29(2): 9-12.

钱锁 . 基于无线传感器网络的智能交通系统介绍[J]. 电子测量技术, 2018,41(5): 102-105.

QIAN S . Introduction on intelligent transportation system based on WSN[J]. Electronic Measurement Technology, 2018,41(5): 102-105.

李洪凤, 魏德健, 张俊忠 ,等. 面向医疗卫生的无线传感器网络节点定位技术的研究进展[J]. 中国医疗设备, 2016,31(3): 80-83.

LI H F, WEI D J, ZHANG J Z ,et al. Research progress of the health-oriented node localization technology with wireless sensor network[J]. China Medical Devices, 2016,31(3): 80-83.

肖倩, 邓见光, 袁华强 . 基于无线传感网络的手机实时医疗监护系统[J]. 电子设计工程, 2013,21(22): 62-65.

XIAO Q, DENG J G, YUAN H Q . Real-time mobile medical monitoring system based on wireless sensor network[J]. Electronic Design Engineering, 2013,21(22): 62-65.

李云焕 . 基于声音识别的交通信息检测技术研究[D]. 西安:长安大学, 2014.

LI Y H . Research on traffic information detection technology based on sound recognition[D]. Xi’an:Chang’an University, 2014.

刘琚, 何振亚 . 盲源分离和盲反卷积[J]. 电子学报, 2002,30(4): 570-576.

LIU J, HE Z Y . A survey of blind source separation and blind deconvolution[J]. Acta Electronica Sinica, 2002,30(4): 570-576.

李振璧, 王康, 姜媛媛 . 盲源分离技术研究与方法综述[J]. 科学技术与工程, 2017,17(14): 141-147.

LI Z B, WANG K, JIANG Y Y . Research and survey on methods of blind source separation technology[J]. Science Technology and Engineering, 2017,17(14): 141-147.

张井伟 . 基于非负矩阵分解的盲源分离算法研究[D]. 秦皇岛:燕山大学, 2016.

ZHANG J W . Blind source separation based on nonnegative matrix decomposition[D]. Qinhuangdao:Yanshan University, 2016.

周晓峰 . 机械振动源的分离和识别方法研究[D]. 杭州:浙江大学, 2012.

ZHOU X F . Research on separation and recognition of mechanical vibration sources[D]. Hangzhou:Zhejiang University, 2012.

LEE D D, SEUNG H S.Learning the parts of objects by non-negative matrix factorization . [J]. Nature, 1999,401(6755): 788-798.

赵知劲, 卢宏, 尚俊娜 . 基于约束 NMF 的欠定盲信号分离算法[J]. 计算机应用研究, 2011,28(5): 1843-1845.

ZHAO Z J, LU H, SHANG J N . Algorithm for underdetermined blind signal separation based on constrained NMF[J]. Application Research of Computers, 2011,28(5): 1843-1845.

FÉVOTTE C, BERTIN N, DURRIEU J L . Nonnegative matrix factorization with the Itakura-Saito divergence:with application to music analysis[J]. Neural Computation, 2009,21(3): 793-825.

SMARAGDIS P . Non-negative matrix factor deconvolution; extraction of multiple sound sources from monophonic inputs[J]. Lecture Notes in Computer Science, 2004(3195): 494-499.

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