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

Tim R Hammond,Warren A Connors,Øivind Midtgaard

doi:10.3390/rs13214359

Abstract

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 m2) 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 (M), 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 M.

Highlights

Sea mines constitute a formidable threat to commercial shipping and naval operations because these weapons are highly effective, low-cost, easy to employ, covert, and widely available [1]
This paper presents a new approach based on Bayesian networks that permits users to evaluate mine countermeasures (MCM) performance
A Bayesian network is a graphical model of the conditional dependencies between a set of discrete random variables, each represented by a network node [22]

Summary

Introduction

Sea mines constitute a formidable threat to commercial shipping and naval operations because these weapons are highly effective, low-cost, easy to employ, covert, and widely available [1]. The primary method to address this threat is through mine sweeping and mine hunting, collectively known as mine countermeasures (MCM). While sweeping is focused on actuating the mines using mechanical or influence methods, mine hunting is a multi-phase process that systematically searches for, identifies, and neutralizes mines. Mine hunting requires the fusion of multiple data processing results from heterogeneous sensors and platforms. The role of MCM evaluation is to assess the overall performance and to communicate the remaining risk to decision makers. Improving the evaluation of mine hunting is the focus of this work

Methods

Results

Conclusion