Closed-Form Pseudolinear Estimators for DRSS-AOA Localization

This paper investigates the hybrid source localization problem using differential received signal strength (DRSS) and angle of arrival (AOA) measurements. The main advantage of hybrid measurements is to improve the localization accuracy with respect to a single sensor modality. For sufficiently shor...

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Autores principales:	Jun Li, Kutluyil Dogancay, Hatem Hmam
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	hybrid localization differential received signal strength localization bearings-only localization maximum likelihood pseudolinear estimator least squares Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/dee017ef17ad4352bc0c83445edf0b7c
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spelling	oai:doaj.org-article:dee017ef17ad4352bc0c83445edf0b7c2021-11-11T19:09:14ZClosed-Form Pseudolinear Estimators for DRSS-AOA Localization10.3390/s212171591424-8220https://doaj.org/article/dee017ef17ad4352bc0c83445edf0b7c2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7159https://doaj.org/toc/1424-8220This paper investigates the hybrid source localization problem using differential received signal strength (DRSS) and angle of arrival (AOA) measurements. The main advantage of hybrid measurements is to improve the localization accuracy with respect to a single sensor modality. For sufficiently short wavelengths, AOA sensors can be constructed with size, weight, power and cost (SWAP-C) requirements in mind, making the proposed hybrid DRSS-AOA sensing feasible at a low cost. Firstly the maximum likelihood estimation solution is derived, which is computationally expensive and likely to become unstable for large noise levels. Then a novel closed-form pseudolinear estimation method is developed by incorporating the AOA measurements into a linearized form of DRSS equations. This method eliminates the nuisance parameter associated with linearized DRSS equations, hence improving the estimation performance. The estimation bias arising from the injection of measurement noise into the pseudolinear data matrix is examined. The method of instrumental variables is employed to reduce this bias. As the performance of the resulting weighted instrumental variable (WIV) estimator depends on the correlation between the IV matrix and data matrix, a selected-hybrid-measurement WIV (SHM-WIV) estimator is proposed to maintain a strong correlation. The superior bias and mean-squared error performance of the new SHM-WIV estimator is illustrated with simulation examples.Jun LiKutluyil DogancayHatem HmamMDPI AGarticlehybrid localizationdifferential received signal strength localizationbearings-only localizationmaximum likelihoodpseudolinear estimatorleast squaresChemical technologyTP1-1185ENSensors, Vol 21, Iss 7159, p 7159 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hybrid localization differential received signal strength localization bearings-only localization maximum likelihood pseudolinear estimator least squares Chemical technology TP1-1185
spellingShingle	hybrid localization differential received signal strength localization bearings-only localization maximum likelihood pseudolinear estimator least squares Chemical technology TP1-1185 Jun Li Kutluyil Dogancay Hatem Hmam Closed-Form Pseudolinear Estimators for DRSS-AOA Localization
description	This paper investigates the hybrid source localization problem using differential received signal strength (DRSS) and angle of arrival (AOA) measurements. The main advantage of hybrid measurements is to improve the localization accuracy with respect to a single sensor modality. For sufficiently short wavelengths, AOA sensors can be constructed with size, weight, power and cost (SWAP-C) requirements in mind, making the proposed hybrid DRSS-AOA sensing feasible at a low cost. Firstly the maximum likelihood estimation solution is derived, which is computationally expensive and likely to become unstable for large noise levels. Then a novel closed-form pseudolinear estimation method is developed by incorporating the AOA measurements into a linearized form of DRSS equations. This method eliminates the nuisance parameter associated with linearized DRSS equations, hence improving the estimation performance. The estimation bias arising from the injection of measurement noise into the pseudolinear data matrix is examined. The method of instrumental variables is employed to reduce this bias. As the performance of the resulting weighted instrumental variable (WIV) estimator depends on the correlation between the IV matrix and data matrix, a selected-hybrid-measurement WIV (SHM-WIV) estimator is proposed to maintain a strong correlation. The superior bias and mean-squared error performance of the new SHM-WIV estimator is illustrated with simulation examples.
format	article
author	Jun Li Kutluyil Dogancay Hatem Hmam
author_facet	Jun Li Kutluyil Dogancay Hatem Hmam
author_sort	Jun Li
title	Closed-Form Pseudolinear Estimators for DRSS-AOA Localization
title_short	Closed-Form Pseudolinear Estimators for DRSS-AOA Localization
title_full	Closed-Form Pseudolinear Estimators for DRSS-AOA Localization
title_fullStr	Closed-Form Pseudolinear Estimators for DRSS-AOA Localization
title_full_unstemmed	Closed-Form Pseudolinear Estimators for DRSS-AOA Localization
title_sort	closed-form pseudolinear estimators for drss-aoa localization
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/dee017ef17ad4352bc0c83445edf0b7c
work_keys_str_mv	AT junli closedformpseudolinearestimatorsfordrssaoalocalization AT kutluyildogancay closedformpseudolinearestimatorsfordrssaoalocalization AT hatemhmam closedformpseudolinearestimatorsfordrssaoalocalization
_version_	1718431615522177024

Closed-Form Pseudolinear Estimators for DRSS-AOA Localization

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