Feature and decision level sensor fusion of electromagnetic induction and ground penetrating radar sensors for landmine detection with hand-held units

Strategies for fusion of electromagnetic induction (metal detector (MD)) and ground penetrating radar (GPR) sensors for landmine detection are investigated. Feature and decision level algorithms are devised and compared. Features are extracted from the MD signals by correlating with weighted density distribution functions. A multi-frequency band linear prediction method generates features for the GPR. Feature level fusion combines MD and GPR features in a single neural network. Decision level fusion is performed by using the MD features as inputs to one neural network and the GPR features as inputs to the geometric mean and combining the output values. Experimental results are reported on a very large real data set containing 2315 mine encounters of different size, shape, content and metal composition that are measured under different soil conditions at three distinct geographical locations.     

一种基于图论的加权聚类融合算法

现有聚类融合算法对混合属性数据进行处理的效果不佳,主要是融合后的结果仍存在一定的分散性。为解决这个问题,提出了一种基于图论的加权聚类融合算法,通过对数据集聚类得到聚类成员后,利用所设计的融合函数对各个数据对象赋予权重,同时通过设置各个数据对间边的权重来确定数据之间的关系,得到带权最近邻图,再用图论的方法进行聚类。实验表明,该算法的聚类精度和稳定性优于其他聚类融合算法。     

数据融合关键技术在靶场实验系统中的应用

通过比较数据融合各算法的优缺点,选择了适用于本系统的数据算法以及应用于一般非线性系统的推广Kalman滤波算法,并分别进行了详尽的描述;结合靶场实验系统的环境以及对于数据融合的要求,基于所选用的数据融合关键技术,提出了解决靶场数据融合问题的可靠性方案;最后通过对靶场数据的计算机仿真充分说明了这种方案的有效性、可行性.     

A comparison of decision-level sensor-fusion methods for anti-personnel landmine detection

We present the sensor-fusion results obtained from measurements within the European research project ground explosive ordinance detection (GEODE) system that strives for the realisation of a vehicle-mounted, multi-sensor, anti-personnel landmine-detection system for humanitarian de-mining. The system has three sensor types: a metal detector (MD), an infrared camera (IR), and a ground penetrating radar (GPR). The output of the sensors is processed to produce confidence levels on a grid covering the test-bed. A confidence level expresses a confidence or belief in a landmine detection on a certain position. The grid with confidence levels is the input for the decision-level sensor-fusion and provides a co-registration of the sensors. The applied fusion methods are naive Bayes' approaches, Dempster–Shafer theory, fuzzy probabilities, a rule-based method, and voting techniques. To compare fusion methods and to analyse the capacity of a method to separate landmines from the background on the basis of the output of different sensors, we provide an analysis of the different methods by viewing them as discriminant functions in the sensor confidence space. The results of experiments on real sensor data are evaluated with the leave-one-out method.     

Information fusion in the immune system

Biologically-inspired methods such as evolutionary algorithms and neural networks are proving useful in the field of information fusion. Artificial immune systems (AISs) are a biologically-inspired approach which take inspiration from the biological immune system. Interestingly, recent research has shown how AISs which use multi-level information sources as input data can be used to build effective algorithms for realtime computer intrusion detection. This research is based on biological information fusion mechanisms used by the human immune system and as such might be of interest to the information fusion community. The aim of this paper is to present a summary of some of the biological information fusion mechanisms seen in the human immune system, and of how these mechanisms have been implemented as AISs.     

基于多切片最优融合集的故障测试约束构造方法

针对故障测试约束构造过程相当复杂,计算开销太大,测试用例设计质量不高,不利于广泛运用,提出一种基于多切片最优融合集的故障测试约束构造方法,采用同一切片标准的不同程序切片构造融合度矩阵来度量切片的一致融合度,通过利用切片融合度、路径条件以及软件故障触发与传播的内在机制进而构造某个故障相应的测试约束,可控制测试用例的规模,提高测试用例设计质量。实验结果表明,这种故障测试约束与传统基于谓词约束和必要性约束比较,生成的测试用例规模较小,很少生成无效测试用例,发现Bug的效率很高。     

A survey of land mine detection technology

This paper describes the state of the art in land mine detection technology and algorithms. Landmine detection is a growing concern due to the danger of buried landmines to people's lives, economic growth and development. Most of the injured people have no connection with the reason why the mines were placed. There are 50–100 million landmines in more than 80 countries around the world. Deactivation is estimated at 100 000 mines per year, against the nearly 2 million mines laid annually. In this paper we describe and analyse sensor technology available including state‐of‐the‐art technology such as ground penetrating radar (GPR), electromagnetic induction (EMI) and nuclear quadrupole resonance (NQR) among others. Robotics, data processing and algorithms are mentioned, considering support vectors, sensor fusion, neural networks, etc. Finally, we establish conclusions highlighting the need to improve not only the way images are acquired, but the way this information is processed and compared.     

An optimized architecture for classification combining data fusion and data-mining

This paper presents a new architecture to integrate a library of feature extraction, Data-mining, and fusion techniques to automatically and optimally configure a classification solution for a given labeled set of training patterns. The most expensive and scarce resource in any detection problem (feature selection/classification) tends to be the acquiring of labeled training patterns from which to design the system. The objective of this paper is to present a new Data-mining architecture that will include conventional Data-mining algorithms, feature selection methods and algorithmic fusion techniques to best exploit the set of labeled training patterns so as to improve the design of the overall classification system. The paper describes how feature selection and Data-mining algorithms are combined through a Genetic Algorithm, using single source data, and how multi-source data are combined through several best-suited fusion techniques by employing a Genetic Algorithm for optimal fusion. A simplified version of the overall system is tested on the detection of volcanoes in the Magellan SAR database of Venus.     

A comparative study on automatic audio–visual fusion for aggression detection using meta-information

Multimodal fusion is a complex topic. For surveillance applications audio–visual fusion is very promising given the complementary nature of the two streams. However, drawing the correct conclusion from multi-sensor data is not straightforward. In previous work we have analysed a database with audio–visual recordings of unwanted behavior in trains (Lefter et al., 2012) and focused on a limited subset of the recorded data. We have collected multi- and unimodal assessments by humans, who have given aggression scores on a 3 point scale. We showed that there are no trivial fusion algorithms to predict the multimodal labels from the unimodal labels since part of the information is lost when using the unimodal streams. We proposed an intermediate step to discover the structure in the fusion process. This step is based upon meta-features and we find a set of five which have an impact on the fusion process. In this paper we extend the findings in (Lefter et al., 2012) for the general case using the entire database. We prove that the meta-features have a positive effect on the fusion process in terms of labels. We then compare three fusion methods that encapsulate the meta-features. They are based on automatic prediction of the intermediate level variables and multimodal aggression from state of the art low level acoustic, linguistic and visual features. The first fusion method is based on applying multiple classifiers to predict intermediate level features from the low level features, and to predict the multimodal label from the intermediate variables. The other two approaches are based on probabilistic graphical models, one using (Dynamic) Bayesian Networks and the other one using Conditional Random Fields. We learn that each approach has its strengths and weaknesses in predicting specific aggression classes and using the meta-features yields significant improvements in all cases.     

交互式多模型异步航迹融合算法

对分布式融合系统中的异步航迹融合算法进行了研究.已有的异步航迹融合算法要求传感器所采用的状态方程与目标运动模式相同,这在实际应用中很难满足.针对已有异步航迹融合算法的不足,建立了基于状态方程集合的异步航迹融合模型.所有传感器均采用基于同一状态方程集合的交互式多模型(IMM)算法来实现航迹生成,融合中心对来自于相同状态方程的异步航迹数据进行加权平均来获得同步航迹,提出了交互式多模型异步航迹融合(IMMASTF)算法.仿真结果验证了IMMASTF算法的有效性.     

Modeling and simulation in support of the design of a data fusion system

This paper uses a modeling and simulation approach in the engineering process of designing a data fusion system for military applications. Fundamental issues in the design of a data fusion system are the selection of appropriate architecture and efficient and dedicated real-time algorithms for the application of interest. Selection of data fusion algorithms is performed using a simulation test bed where realistic modeling of sensor performance is being performed. The test bed also allows the use of multiple algorithms in a dynamic sense based on the particular regime in which the fusion system is operating. This paper first presents the data fusion system engineering guidelines that are being used. Then the paper addresses the complex problem of the tactical picture compilation. Finally the paper shows a case study where the selection of data fusion algorithms is performed for an airborne platform.     

基于Stacking融合模型的Web攻击检测方法

随着计算机技术与互联网技术的飞速发展,Web应用在人们的生产与生活中扮演着越来越重要的角色。但是在人们的日常生活与工作中带来了更多便捷的同时,却也带来了严重的安全隐患。在开发Web应用的过程中,大量不规范的新技术应用引入了很多的网站漏洞。攻击者可以利用Web应用开发过程中的漏洞发起攻击,当Web应用受到攻击时会造成严重的数据泄露和财产损失等安全问题,因此Web安全问题一直受到学术界和工业界的关注。超文本传输协议(HTTP)是一种在Web应用中广泛使用的应用层协议。随着HTTP协议的大量使用,在HTTP请求数据中包含了大量的实际入侵,针对HTTP请求数据进行Web攻击检测的研究也开始逐渐被研究人员所重视。本文提出了一种基于Stacking融合模型的Web攻击检测方法,针对每一条文本格式的HTTP请求数据,首先进行格式化处理得到既定的格式,结合使用Word2Vec方法和TextCNN模型将其转换成向量化表示形式;然后利用Stacking模型融合方法,将不同的子模型(使用配置不同尺寸过滤器的Text-CNN模型搭配不同的检测算法)进行融合搭建出Web攻击检测模型,与融合之前单独的子模型相比在准确率、召回率、F1值上都有所提升。本文所提出的Web攻击检测模型在公开数据集和真实环境数据上都取得了更加稳定的检测性能。     

基于深度学习的图像融合方法综述

图像融合技术旨在将不同源图像中的互补信息整合到单幅融合图像中以全面表征成像场景,并促进后续的视觉任务。随着深度学习的兴起,基于深度学习的图像融合算法如雨后春笋般涌现,特别是自编码器、生成对抗网络以及Transformer等技术的出现使图像融合性能产生了质的飞跃。本文对不同融合任务场景下的前沿深度融合算法进行全面论述和分析。首先,介绍图像融合的基本概念以及不同融合场景的定义。针对多模图像融合、数字摄影图像融合以及遥感影像融合等不同的融合场景,从网络架构和监督范式等角度全面阐述各类方法的基本思想,并讨论各类方法的特点。其次,总结各类算法的局限性,并给出进一步的改进方向。再次,简要介绍不同融合场景中常用的数据集,并给出各种评估指标的具体定义。对于每一种融合任务,从定性评估、定量评估和运行效率等多角度全面比较其中代表性算法的性能。本文提及的算法、数据集和评估指标已汇总至https://github.com/Linfeng-Tang/Image-Fusion。最后,给出了本文结论以及图像融合研究中存在的一些严峻挑战,并对未来可能的研究方向进行了展望。     

基于光谱角和光谱距离评价指标的遥感影像融合方法比较研究——以QuickBird数据为例

遥感数据融合是一种得到具有较高空间分辨率和光谱分辨率数据的有效方法,而如何保持地物光谱特性是遥感数据融合的关键问题。QuickBird卫星数据是高空间分辨率遥感数据的典型代表,探讨QuickBird数据的融合方法对于促进该数据的广泛应用具有重要意义,同时也能为其他高分辨率数据的处理提供借鉴。以QuickBird高分辨遥感数据为例,比较研究了目前针对高分辨率遥感数据常用的高通滤波、小波变换、Gram\|Schmidt和Pan\|sharpening 4种融合方法,以反映光谱曲线变化程度的光谱角和光谱距离为指标,评价了4种融合方法对多光谱影像地物光谱信息的保持能力。结果表明:小波变换在显著提高空间分辨率的同时最大程度地保持了原始多光谱影像的光谱信息,是4种方法中最适合QuickBird遥感数据的融合方法。     

多传感器集中式增量卡尔曼滤波融合算法

在单个传感器的状态估计系统中,标准的增量卡尔曼滤波方法可以有效消除量测系统误差。对于多传感器情况,标准算法失效。针对该问题,提出了多传感器集中式增量卡尔曼滤波融合算法,即:增量卡尔曼滤波的扩维融合算法和增量卡尔曼滤波的序贯融合算法。在标准增量卡尔曼滤波算法的基础上,结合扩维融合和序贯融合的思想来实现多传感器数据的融合。实验结果表明,当存在量测系统误差时,提出的集中式融合算法与传统的集中式融合算法相比,提高了滤波精度,并且能够成功地消除量测系统误差。     

Steganography algorithms recognition based on match image and deep features verification

Steganography algorithms recognition is a sub-section of steganalysis. Analysis shows when a steganalysis detector trained on one cover source is applied to images from an unseen source, generally the detection performance decreases. To tackle with this problem, this paper proposes a steganalytic scheme for steganography algorithms recognition. For a given testing image, a match image of the testing image is achieved. The match image is generated by performing a Gaussian filtering on the testing image to remove the possible stego signal. Then the match image is embedded in with recognized steganography algorithms. A CNN model trained on a training set is used to extract deep features from testing image and match images. Computing similarity between features with inner product operation or weighted-χ², the final decision is made according to similarity between testing feature and each class of match feature. The proposed scheme can also detect steganography algorithms unknown in training set. Experiments show that, comparing with directly used CNN model, the proposed scheme achieves considerable improvement on testing accuracy when detecting images come from unseen source.     

可见光与红外图像融合质量评价指标分析

目的 客观评价作为图像融合的重要研究领域,是评价融合算法性能的有力工具。目前,已有几十种不同类型的评价指标,但各应用领域包括可见光与红外图像融合,仍缺少统一的选择依据。为了方便比较不同融合算法性能,提出一种客观评价指标的通用分析方法并应用于可见光与红外图像融合。方法 将可见光与红外图像基准数据集中的客观评价指标分为两类,分别是基于融合图像的评价指标与基于源图像和融合图像的评价指标。采用Kendall相关系数分析融合指标间的相关性,聚类得到指标分组;采用Borda计数排序法统计算法的综合排序,分析单一指标排序和综合排序的相关性,得到一致性较高的指标集合;采用离散系数分析指标均值随不同算法的波动程度,选择充分体现不同算法间差异的指标;综合相关性分析、一致性分析及离散系数分析,总结具有代表性的建议指标集合。结果 在13对彩色可见光与红外和8对灰度可见光与红外两组图像源中,分别统计分析不同图像融合算法的客观评价数据,得到可见光与红外图像融合的建议指标集(标准差、边缘保持度),作为融合算法性能评估的重要参考。相较于现有方法,实验覆盖20种融合算法和13种客观评价指标,并且不依赖主观评价结果。结论...     

耦合图像间的数据融合算法

耦合数据的融合算法主要通过利用其中一个数据集的信息提高对其他耦合数据集的估计精度和完善对相关潜变量的解释。针对现实中存在的大量耦合图像,基于耦合数据融合中的耦合矩阵和张量分解优化（CMTF-OPT）算法,提出一种针对耦合图像的耦合图像分解优化（CIF-OPT）算法。相应的理论分析及实验结果表明,不同噪声影响下用CIF-OPT算法进行耦合图像融合后的效果均具有鲁棒性,且融合效果优于其他耦合算法（如：CMTF-OPT算法）。特别地,针对其中缺失数据元素的图像,CIF-OPT算法可以利用与其耦合的图像,对缺失数据元素的图像进行精确的数据恢复。     

A mathematical framework to optimize ATR systems with non-declarations and sensor fusion

Combat identification is one example where incorrect automatic target recognition (ATR) output labels may have substantial decision costs. For example, the incorrect labeling of hostile targets vs. friendly non-targets may have high costs; yet, these costs are difficult to quantify. One way to increase decision confidence is through fusion of data from multiple sources or from multiple looks through time. Numerous methods have been published to determine a Bayes’ optimal fusion decision if decision costs are well known. This research presents a novel mathematical programming ATR evaluation framework. A new objective function inclusive of time is introduced to optimize and compare ATR systems. Constraints are developed to enforce both decision maker preferences and traditional engineering measures of performance. This research merges rejection and receiver operating characteristic (ROC) analysis by incorporating rejection and ROC thresholds as decision variables. The rejection thresholds specify non-declaration regions, while the ROC thresholds explore viable true positive and false positive tradeoffs for output target labels. This methodology yields an optimal ATR system subject to decision maker constraints without using explicit costs for each type of output decision. A sample application is included for the fusion of two channels of collected polarized radar data for 10 different ground targets. A Boolean logic and probabilistic neural network fusion method are optimized and compared. Sensitivity analysis of significant performance parameters then reveals preferred regions for each of the fusion algorithms.     

Data fusion of geotechnical and geophysical data for three-dimensional subsoil schematisations

Subsoil schematisations are a paramount activity for the execution of infrastructure earthworks. Currently, subsoil schematisations are laborious, and mostly based on correlation and interpolation of available geotechnical and geophysical in-situ data. Geotechnical in-situ testing tends to be accurate but merely provides local information, while geophysical investigations are often performed to image the subsurface, and provide valuable insight for areas. Yet, there is not always a clear way to reflect geophysical properties to soil schematisation. This paper presents a data fusion methodology to perform subsoil schematisation and parametrisation. This methodology makes use of available geotechnical, geological and geophysical data sets, and combines them by means of machine learning algorithms (Neural Networks and Random Forest). The data fusion method is applied on two case studies. The first case study concerns a large area of a flood defence line, and the second case study concerns a small polder area next to a dike. The accuracy of the data fusion algorithm is assessed by comparing its results against a validation data set, that has not been exposed to the data fusion algorithm. The performance of the two algorithms and of the parameters that govern each algorithm is discussed. The results show that both Neural Networks and Random Forest are suitable to perform subsoil schematisations. The analyses show that the Random Forest leads to a lower error on the validation data set. However, Random Forest fails to predict the occurrence of thin clay layers in the second case study, while Neural Networks are successful at it. The data fusion methodology shows the potential to enhance the subsoil schematisation procedure, by increasing the schematisation spatial resolution.