Direction of arrival estimation in passive radar based on deep neural network

Abstract Most traditional direction of arrival (DOA) estimation methods in passive radar are based on the parametric model of the antenna array manifold, and lack the adaption to the array errors. The data‐driven machine learning‐based methods have great array error adaption capability. However, mos...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xiaoyong Lyu, Jun Wang
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/5bceb2daf5164291b9f8a8841f4b7316
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5bceb2daf5164291b9f8a8841f4b7316
record_format	dspace
spelling	oai:doaj.org-article:5bceb2daf5164291b9f8a8841f4b73162021-11-09T10:16:47ZDirection of arrival estimation in passive radar based on deep neural network1751-96831751-967510.1049/sil2.12065https://doaj.org/article/5bceb2daf5164291b9f8a8841f4b73162021-12-01T00:00:00Zhttps://doi.org/10.1049/sil2.12065https://doaj.org/toc/1751-9675https://doaj.org/toc/1751-9683Abstract Most traditional direction of arrival (DOA) estimation methods in passive radar are based on the parametric model of the antenna array manifold, and lack the adaption to the array errors. The data‐driven machine learning‐based methods have great array error adaption capability. However, most existing machine learning‐based methods cannot be applied directly to the passive radar DOA estimation, because the array covariance matrix that they use as the input is not easy to estimate with adequate accuracy in passive radar owing to the poor target signal to clutter plus noise ratio (SCNR). A deep learning‐based method for DOA estimation in passive radar is proposed here. Clutter cancelation and range–Doppler cross‐correlation (RDCC) is performed to increase the target SCNR. The RDCC result is taken as the input of the deep learning method, and the amplitude and phase uncertainties of the RDCC result are treated. A two‐stage deep neural network (DNN) is designed. The first stage determines the spatial sub‐region of the target, and the second stage gets the refined DOA estimation. Simulations show that the proposed two‐stage DNN well outperforms the traditional passive radar DOA estimation method and the multi‐layer perceptron network. Real experiments verify the superiority of the proposed method.Xiaoyong LyuJun WangWileyarticleTelecommunicationTK5101-6720ENIET Signal Processing, Vol 15, Iss 9, Pp 612-621 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Telecommunication TK5101-6720
spellingShingle	Telecommunication TK5101-6720 Xiaoyong Lyu Jun Wang Direction of arrival estimation in passive radar based on deep neural network
description	Abstract Most traditional direction of arrival (DOA) estimation methods in passive radar are based on the parametric model of the antenna array manifold, and lack the adaption to the array errors. The data‐driven machine learning‐based methods have great array error adaption capability. However, most existing machine learning‐based methods cannot be applied directly to the passive radar DOA estimation, because the array covariance matrix that they use as the input is not easy to estimate with adequate accuracy in passive radar owing to the poor target signal to clutter plus noise ratio (SCNR). A deep learning‐based method for DOA estimation in passive radar is proposed here. Clutter cancelation and range–Doppler cross‐correlation (RDCC) is performed to increase the target SCNR. The RDCC result is taken as the input of the deep learning method, and the amplitude and phase uncertainties of the RDCC result are treated. A two‐stage deep neural network (DNN) is designed. The first stage determines the spatial sub‐region of the target, and the second stage gets the refined DOA estimation. Simulations show that the proposed two‐stage DNN well outperforms the traditional passive radar DOA estimation method and the multi‐layer perceptron network. Real experiments verify the superiority of the proposed method.
format	article
author	Xiaoyong Lyu Jun Wang
author_facet	Xiaoyong Lyu Jun Wang
author_sort	Xiaoyong Lyu
title	Direction of arrival estimation in passive radar based on deep neural network
title_short	Direction of arrival estimation in passive radar based on deep neural network
title_full	Direction of arrival estimation in passive radar based on deep neural network
title_fullStr	Direction of arrival estimation in passive radar based on deep neural network
title_full_unstemmed	Direction of arrival estimation in passive radar based on deep neural network
title_sort	direction of arrival estimation in passive radar based on deep neural network
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/5bceb2daf5164291b9f8a8841f4b7316
work_keys_str_mv	AT xiaoyonglyu directionofarrivalestimationinpassiveradarbasedondeepneuralnetwork AT junwang directionofarrivalestimationinpassiveradarbasedondeepneuralnetwork
_version_	1718441109889220608

Direction of arrival estimation in passive radar based on deep neural network

Ejemplares similares