A reduced dimension multiple signal classification–based direct location algorithm with dense arrays

Aiming at the issue of parameter matching in conventional two-step location, a reduced dimension multiple signal classification direct position determination algorithm based on multi-array is proposed. Based on the idea of dimension reduction, the algorithm avoids multi-dimensional search in spatial...

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Main Authors: Jianfeng Li, Gaofeng Zhao, Baobao Li, Xianpeng Wang, Mengxing Huang
Format: Article
Language:English
Published: Wiley 2022-05-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1177/15501329221097583
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author Jianfeng Li
Gaofeng Zhao
Baobao Li
Xianpeng Wang
Mengxing Huang
author_facet Jianfeng Li
Gaofeng Zhao
Baobao Li
Xianpeng Wang
Mengxing Huang
author_sort Jianfeng Li
collection DOAJ
description Aiming at the issue of parameter matching in conventional two-step location, a reduced dimension multiple signal classification direct position determination algorithm based on multi-array is proposed. Based on the idea of dimension reduction, the algorithm avoids multi-dimensional search in spatial domain and attenuation coefficient domain and reduces the search complexity. Simulation results show that the performance of the algorithm is better than the traditional angle of arrival two-step localization algorithm and subspace data fusion direct localization algorithm.
format Article
id doaj-art-551df7e16d6d450382ea1476edfb414c
institution Kabale University
issn 1550-1477
language English
publishDate 2022-05-01
publisher Wiley
record_format Article
series International Journal of Distributed Sensor Networks
spelling doaj-art-551df7e16d6d450382ea1476edfb414c2025-08-20T03:34:20ZengWileyInternational Journal of Distributed Sensor Networks1550-14772022-05-011810.1177/15501329221097583A reduced dimension multiple signal classification–based direct location algorithm with dense arraysJianfeng Li0Gaofeng Zhao1Baobao Li2Xianpeng Wang3Mengxing Huang4College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaState Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, ChinaState Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, ChinaAiming at the issue of parameter matching in conventional two-step location, a reduced dimension multiple signal classification direct position determination algorithm based on multi-array is proposed. Based on the idea of dimension reduction, the algorithm avoids multi-dimensional search in spatial domain and attenuation coefficient domain and reduces the search complexity. Simulation results show that the performance of the algorithm is better than the traditional angle of arrival two-step localization algorithm and subspace data fusion direct localization algorithm.https://doi.org/10.1177/15501329221097583
spellingShingle Jianfeng Li
Gaofeng Zhao
Baobao Li
Xianpeng Wang
Mengxing Huang
A reduced dimension multiple signal classification–based direct location algorithm with dense arrays
International Journal of Distributed Sensor Networks
title A reduced dimension multiple signal classification–based direct location algorithm with dense arrays
title_full A reduced dimension multiple signal classification–based direct location algorithm with dense arrays
title_fullStr A reduced dimension multiple signal classification–based direct location algorithm with dense arrays
title_full_unstemmed A reduced dimension multiple signal classification–based direct location algorithm with dense arrays
title_short A reduced dimension multiple signal classification–based direct location algorithm with dense arrays
title_sort reduced dimension multiple signal classification based direct location algorithm with dense arrays
url https://doi.org/10.1177/15501329221097583
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