A Sliding Window Data Compression Method for Spatial-Time DOA Estimation

This paper presents a sliding window data compression method for spatial-time direction-of-arrival (DOA) estimation using coprime array. The signal model is firstly formulated by jointly using the temporal and spatial information of the impinging sources. Then, a sliding window data compression proc...

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Main Authors: Pin-Jiao Zhao, Guo-Bing Hu, Li-Wei Wang
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:International Journal of Antennas and Propagation
Online Access:http://dx.doi.org/10.1155/2021/9705617
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author Pin-Jiao Zhao
Guo-Bing Hu
Li-Wei Wang
author_facet Pin-Jiao Zhao
Guo-Bing Hu
Li-Wei Wang
author_sort Pin-Jiao Zhao
collection DOAJ
description This paper presents a sliding window data compression method for spatial-time direction-of-arrival (DOA) estimation using coprime array. The signal model is firstly formulated by jointly using the temporal and spatial information of the impinging sources. Then, a sliding window data compression processing is performed on the array output matrix to realize fast calculation of time average function, and the computational burden has been reduced accordingly. Based on the concept of sum and difference co-array (SDCA), the vectorized conjugate augmented MUSIC is adopted, with which more sources than twice of the physical sensors can be resolved. Additionally, the sparse array robustness to sensor failure has been evaluated by introducing the concept of essential sensors. The theoretical analysis and numerical simulations are provided to confirm the effectiveness performance of the proposed method.
format Article
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institution OA Journals
issn 1687-5877
language English
publishDate 2021-01-01
publisher Wiley
record_format Article
series International Journal of Antennas and Propagation
spelling doaj-art-64f91c8c873242528ea4e1ad34d9a7352025-08-20T02:06:39ZengWileyInternational Journal of Antennas and Propagation1687-58772021-01-01202110.1155/2021/9705617A Sliding Window Data Compression Method for Spatial-Time DOA EstimationPin-Jiao Zhao0Guo-Bing Hu1Li-Wei Wang2Department of Electronic and Information EngineeringDepartment of Electronic and Information EngineeringNanjing Electronic Devices InstituteThis paper presents a sliding window data compression method for spatial-time direction-of-arrival (DOA) estimation using coprime array. The signal model is firstly formulated by jointly using the temporal and spatial information of the impinging sources. Then, a sliding window data compression processing is performed on the array output matrix to realize fast calculation of time average function, and the computational burden has been reduced accordingly. Based on the concept of sum and difference co-array (SDCA), the vectorized conjugate augmented MUSIC is adopted, with which more sources than twice of the physical sensors can be resolved. Additionally, the sparse array robustness to sensor failure has been evaluated by introducing the concept of essential sensors. The theoretical analysis and numerical simulations are provided to confirm the effectiveness performance of the proposed method.http://dx.doi.org/10.1155/2021/9705617
spellingShingle Pin-Jiao Zhao
Guo-Bing Hu
Li-Wei Wang
A Sliding Window Data Compression Method for Spatial-Time DOA Estimation
International Journal of Antennas and Propagation
title A Sliding Window Data Compression Method for Spatial-Time DOA Estimation
title_full A Sliding Window Data Compression Method for Spatial-Time DOA Estimation
title_fullStr A Sliding Window Data Compression Method for Spatial-Time DOA Estimation
title_full_unstemmed A Sliding Window Data Compression Method for Spatial-Time DOA Estimation
title_short A Sliding Window Data Compression Method for Spatial-Time DOA Estimation
title_sort sliding window data compression method for spatial time doa estimation
url http://dx.doi.org/10.1155/2021/9705617
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AT liweiwang aslidingwindowdatacompressionmethodforspatialtimedoaestimation
AT pinjiaozhao slidingwindowdatacompressionmethodforspatialtimedoaestimation
AT guobinghu slidingwindowdatacompressionmethodforspatialtimedoaestimation
AT liweiwang slidingwindowdatacompressionmethodforspatialtimedoaestimation