Intelligence-Aware Batch Processing for TMA with Bearings-Only Measurements

This paper develops a framework to track the trajectory of a target in 2D by considering a moving ownship able to measure bearing measurements. Notably, the framework allows one to incorporate additional information (e.g., obtained via intelligence) such as knowledge on the fact the target’s traject...

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Autores principales:	Gabriele Oliva, Alfonso Farina, Roberto Setola
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	target motion analysis intelligence-aware estimation radar Cramér–Rao lower bound nonlinear estimation constrained MLE Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/a5b35d83d5a84c318ff05910fcebedb3
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id	oai:doaj.org-article:a5b35d83d5a84c318ff05910fcebedb3
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spelling	oai:doaj.org-article:a5b35d83d5a84c318ff05910fcebedb32021-11-11T19:11:36ZIntelligence-Aware Batch Processing for TMA with Bearings-Only Measurements10.3390/s212172111424-8220https://doaj.org/article/a5b35d83d5a84c318ff05910fcebedb32021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7211https://doaj.org/toc/1424-8220This paper develops a framework to track the trajectory of a target in 2D by considering a moving ownship able to measure bearing measurements. Notably, the framework allows one to incorporate additional information (e.g., obtained via intelligence) such as knowledge on the fact the target’s trajectory is contained in the intersection of some sets or the fact it lies outside the union of other sets. The approach is formally characterized by providing a constrained maximum likelihood estimation (MLE) formulation and by extending the definition of the Cramér–Rao lower bound (CRLB) matrix to the case of MLE problems with inequality constraints, relying on the concept of generalized Jacobian matrix. Moreover, based on the additional information, the ownship motion is chosen by mimicking the Artificial Potential Fields technique that is typically used by mobile robots to aim at a goal (in this case, the region where the target is assumed to be) while avoiding obstacles (i.e., the region that is assumed not to intersect the target’s trajectory). In order to show the effectiveness of the proposed approach, the paper is complemented by a simulation campaign where the MLE computations are carried out via an evolutionary ant colony optimization software, namely, mixed-integer distributed ant colony optimization solver (MIDACO-SOLVER). As a result, the proposed framework exhibits remarkably better performance, and in particular, we observe that the solution is less likely to remain stuck in unsatisfactory local minima during the MLE computation.Gabriele OlivaAlfonso FarinaRoberto SetolaMDPI AGarticletarget motion analysisintelligence-aware estimationradarCramér–Rao lower boundnonlinear estimationconstrained MLEChemical technologyTP1-1185ENSensors, Vol 21, Iss 7211, p 7211 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	target motion analysis intelligence-aware estimation radar Cramér–Rao lower bound nonlinear estimation constrained MLE Chemical technology TP1-1185
spellingShingle	target motion analysis intelligence-aware estimation radar Cramér–Rao lower bound nonlinear estimation constrained MLE Chemical technology TP1-1185 Gabriele Oliva Alfonso Farina Roberto Setola Intelligence-Aware Batch Processing for TMA with Bearings-Only Measurements
description	This paper develops a framework to track the trajectory of a target in 2D by considering a moving ownship able to measure bearing measurements. Notably, the framework allows one to incorporate additional information (e.g., obtained via intelligence) such as knowledge on the fact the target’s trajectory is contained in the intersection of some sets or the fact it lies outside the union of other sets. The approach is formally characterized by providing a constrained maximum likelihood estimation (MLE) formulation and by extending the definition of the Cramér–Rao lower bound (CRLB) matrix to the case of MLE problems with inequality constraints, relying on the concept of generalized Jacobian matrix. Moreover, based on the additional information, the ownship motion is chosen by mimicking the Artificial Potential Fields technique that is typically used by mobile robots to aim at a goal (in this case, the region where the target is assumed to be) while avoiding obstacles (i.e., the region that is assumed not to intersect the target’s trajectory). In order to show the effectiveness of the proposed approach, the paper is complemented by a simulation campaign where the MLE computations are carried out via an evolutionary ant colony optimization software, namely, mixed-integer distributed ant colony optimization solver (MIDACO-SOLVER). As a result, the proposed framework exhibits remarkably better performance, and in particular, we observe that the solution is less likely to remain stuck in unsatisfactory local minima during the MLE computation.
format	article
author	Gabriele Oliva Alfonso Farina Roberto Setola
author_facet	Gabriele Oliva Alfonso Farina Roberto Setola
author_sort	Gabriele Oliva
title	Intelligence-Aware Batch Processing for TMA with Bearings-Only Measurements
title_short	Intelligence-Aware Batch Processing for TMA with Bearings-Only Measurements
title_full	Intelligence-Aware Batch Processing for TMA with Bearings-Only Measurements
title_fullStr	Intelligence-Aware Batch Processing for TMA with Bearings-Only Measurements
title_full_unstemmed	Intelligence-Aware Batch Processing for TMA with Bearings-Only Measurements
title_sort	intelligence-aware batch processing for tma with bearings-only measurements
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a5b35d83d5a84c318ff05910fcebedb3
work_keys_str_mv	AT gabrieleoliva intelligenceawarebatchprocessingfortmawithbearingsonlymeasurements AT alfonsofarina intelligenceawarebatchprocessingfortmawithbearingsonlymeasurements AT robertosetola intelligenceawarebatchprocessingfortmawithbearingsonlymeasurements
_version_	1718431602224136192

Intelligence-Aware Batch Processing for TMA with Bearings-Only Measurements

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