A Bayesian Network Approach to Evaluating the Effectiveness of Modern Mine Hunting

This paper describes a novel technique for estimating how many mines remain after a full or partial underwater mine hunting operation. The technique applies Bayesian fusion of all evidence from the heterogeneous sensor systems used for detection, classification, and identification of mines. It relie...

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Autores principales:	Tim R. Hammond, Øivind Midtgaard, Warren A. Connors
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	bayesian network naval mine countermeasures risk through-the-sensor evaluation Science Q
Acceso en línea:	https://doaj.org/article/a60030bfff1a48cbb7a1461bdbbdf71e
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spelling	oai:doaj.org-article:a60030bfff1a48cbb7a1461bdbbdf71e2021-11-11T18:54:37ZA Bayesian Network Approach to Evaluating the Effectiveness of Modern Mine Hunting10.3390/rs132143592072-4292https://doaj.org/article/a60030bfff1a48cbb7a1461bdbbdf71e2021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4359https://doaj.org/toc/2072-4292This paper describes a novel technique for estimating how many mines remain after a full or partial underwater mine hunting operation. The technique applies Bayesian fusion of all evidence from the heterogeneous sensor systems used for detection, classification, and identification of mines. It relies on through-the-sensor (TTS) assessment, by which the sensors’ performances can be measured in situ through processing of their recorded data, yielding the local mine recognition probability, and false alarm rate. The method constructs a risk map of the minefield area composed of small grid cells (~4 m<sup>2</sup>) that are colour coded according to the remaining mine probability. The new approach can produce this map using the available evidence whenever decision support is needed during the mine hunting operation, e.g., for replanning purposes. What distinguishes the new technique from other recent TTS methods is its use of Bayesian networks that facilitate more complex reasoning within each grid cell. These networks thus allow for the incorporation of two types of evidence not previously considered in evaluation: the explosions that typically result from mine neutralization and verification of mine destruction by visual/sonar inspection. A simulation study illustrates how these additional pieces of evidence lead to the improved estimation of the number of deployed mines (<i>M</i>), compared to results from two recent TTS evaluation approaches that do not use them. Estimation performance was assessed using the mean squared error (MSE) in estimates of <i>M</i>.Tim R. HammondØivind MidtgaardWarren A. ConnorsMDPI AGarticlebayesian networknaval mine countermeasuresriskthrough-the-sensor evaluationScienceQENRemote Sensing, Vol 13, Iss 4359, p 4359 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bayesian network naval mine countermeasures risk through-the-sensor evaluation Science Q
spellingShingle	bayesian network naval mine countermeasures risk through-the-sensor evaluation Science Q Tim R. Hammond Øivind Midtgaard Warren A. Connors A Bayesian Network Approach to Evaluating the Effectiveness of Modern Mine Hunting
description	This paper describes a novel technique for estimating how many mines remain after a full or partial underwater mine hunting operation. The technique applies Bayesian fusion of all evidence from the heterogeneous sensor systems used for detection, classification, and identification of mines. It relies on through-the-sensor (TTS) assessment, by which the sensors’ performances can be measured in situ through processing of their recorded data, yielding the local mine recognition probability, and false alarm rate. The method constructs a risk map of the minefield area composed of small grid cells (~4 m<sup>2</sup>) that are colour coded according to the remaining mine probability. The new approach can produce this map using the available evidence whenever decision support is needed during the mine hunting operation, e.g., for replanning purposes. What distinguishes the new technique from other recent TTS methods is its use of Bayesian networks that facilitate more complex reasoning within each grid cell. These networks thus allow for the incorporation of two types of evidence not previously considered in evaluation: the explosions that typically result from mine neutralization and verification of mine destruction by visual/sonar inspection. A simulation study illustrates how these additional pieces of evidence lead to the improved estimation of the number of deployed mines (<i>M</i>), compared to results from two recent TTS evaluation approaches that do not use them. Estimation performance was assessed using the mean squared error (MSE) in estimates of <i>M</i>.
format	article
author	Tim R. Hammond Øivind Midtgaard Warren A. Connors
author_facet	Tim R. Hammond Øivind Midtgaard Warren A. Connors
author_sort	Tim R. Hammond
title	A Bayesian Network Approach to Evaluating the Effectiveness of Modern Mine Hunting
title_short	A Bayesian Network Approach to Evaluating the Effectiveness of Modern Mine Hunting
title_full	A Bayesian Network Approach to Evaluating the Effectiveness of Modern Mine Hunting
title_fullStr	A Bayesian Network Approach to Evaluating the Effectiveness of Modern Mine Hunting
title_full_unstemmed	A Bayesian Network Approach to Evaluating the Effectiveness of Modern Mine Hunting
title_sort	bayesian network approach to evaluating the effectiveness of modern mine hunting
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a60030bfff1a48cbb7a1461bdbbdf71e
work_keys_str_mv	AT timrhammond abayesiannetworkapproachtoevaluatingtheeffectivenessofmodernminehunting AT øivindmidtgaard abayesiannetworkapproachtoevaluatingtheeffectivenessofmodernminehunting AT warrenaconnors abayesiannetworkapproachtoevaluatingtheeffectivenessofmodernminehunting AT timrhammond bayesiannetworkapproachtoevaluatingtheeffectivenessofmodernminehunting AT øivindmidtgaard bayesiannetworkapproachtoevaluatingtheeffectivenessofmodernminehunting AT warrenaconnors bayesiannetworkapproachtoevaluatingtheeffectivenessofmodernminehunting
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A Bayesian Network Approach to Evaluating the Effectiveness of Modern Mine Hunting

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