Radar target shape recognition using a gated recurrent unit based on RCS time series' statistical features by sliding window segmentation

Abstract The radar cross section (RCS) is an important parameter that reflects the scattering characteristics of radar targets. Based on the monostatic radar RCS time series' statistical features by sliding window segmentation, a novel sliding window‐statistical‐gated recurrent unit (SW‐S‐GRU)...

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Autores principales:	Lv Ye, ShengBo Hu, TingTing Yan, Xin Meng, ManQin Zhu, RuiZe Xu
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/12bd1a2712d447b09e8b42da9959555b
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spelling	oai:doaj.org-article:12bd1a2712d447b09e8b42da9959555b2021-11-12T15:34:30ZRadar target shape recognition using a gated recurrent unit based on RCS time series' statistical features by sliding window segmentation1751-87921751-878410.1049/rsn2.12159https://doaj.org/article/12bd1a2712d447b09e8b42da9959555b2021-12-01T00:00:00Zhttps://doi.org/10.1049/rsn2.12159https://doaj.org/toc/1751-8784https://doaj.org/toc/1751-8792Abstract The radar cross section (RCS) is an important parameter that reflects the scattering characteristics of radar targets. Based on the monostatic radar RCS time series' statistical features by sliding window segmentation, a novel sliding window‐statistical‐gated recurrent unit (SW‐S‐GRU) method for radar target recognition (RTR) is proposed using a GRU. The sliding window method divides the RCS time series into many segments for obtaining the statistical features as the input of the GRU. The deeper features are extracted from the segmented time series and then classified by the GRU. Under the model of precession motion, simulation results show that the proposed method achieves a great recognition accuracy. Even under the condition of a low SNR, the proposed method can obtain better recognition accuracy. For the precession dataset, the effect of sliding window parameters on RTR is analysed. The simulation results show that the recognition result of the model is better using the longer series overlap length between adjacent times. And when the number of hidden layers is 8 or above 8, the recognition accuracy does not change. Using the publicly available measured drone RCS data, the proposed method can also identify the drone well.Lv YeShengBo HuTingTing YanXin MengManQin ZhuRuiZe XuWileyarticleTelecommunicationTK5101-6720ENIET Radar, Sonar & Navigation, Vol 15, Iss 12, Pp 1715-1726 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Telecommunication TK5101-6720
spellingShingle	Telecommunication TK5101-6720 Lv Ye ShengBo Hu TingTing Yan Xin Meng ManQin Zhu RuiZe Xu Radar target shape recognition using a gated recurrent unit based on RCS time series' statistical features by sliding window segmentation
description	Abstract The radar cross section (RCS) is an important parameter that reflects the scattering characteristics of radar targets. Based on the monostatic radar RCS time series' statistical features by sliding window segmentation, a novel sliding window‐statistical‐gated recurrent unit (SW‐S‐GRU) method for radar target recognition (RTR) is proposed using a GRU. The sliding window method divides the RCS time series into many segments for obtaining the statistical features as the input of the GRU. The deeper features are extracted from the segmented time series and then classified by the GRU. Under the model of precession motion, simulation results show that the proposed method achieves a great recognition accuracy. Even under the condition of a low SNR, the proposed method can obtain better recognition accuracy. For the precession dataset, the effect of sliding window parameters on RTR is analysed. The simulation results show that the recognition result of the model is better using the longer series overlap length between adjacent times. And when the number of hidden layers is 8 or above 8, the recognition accuracy does not change. Using the publicly available measured drone RCS data, the proposed method can also identify the drone well.
format	article
author	Lv Ye ShengBo Hu TingTing Yan Xin Meng ManQin Zhu RuiZe Xu
author_facet	Lv Ye ShengBo Hu TingTing Yan Xin Meng ManQin Zhu RuiZe Xu
author_sort	Lv Ye
title	Radar target shape recognition using a gated recurrent unit based on RCS time series' statistical features by sliding window segmentation
title_short	Radar target shape recognition using a gated recurrent unit based on RCS time series' statistical features by sliding window segmentation
title_full	Radar target shape recognition using a gated recurrent unit based on RCS time series' statistical features by sliding window segmentation
title_fullStr	Radar target shape recognition using a gated recurrent unit based on RCS time series' statistical features by sliding window segmentation
title_full_unstemmed	Radar target shape recognition using a gated recurrent unit based on RCS time series' statistical features by sliding window segmentation
title_sort	radar target shape recognition using a gated recurrent unit based on rcs time series' statistical features by sliding window segmentation
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/12bd1a2712d447b09e8b42da9959555b
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Radar target shape recognition using a gated recurrent unit based on RCS time series' statistical features by sliding window segmentation

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