Multisensor data fusion for surface land-mine detection

Receiver operating characteristic (ROC) curves have been used to examine a novel target recognition system using a number of knowledge-based techniques to automatically detect surface land mines that are present in 30 sets of thermal and multispectral images. A summary of the results, graphed at a probability of detection greater than or equal to 96%, shows the false-alarm rates (FARs) obtained using various combinations of fusing sensors and neural classifiers averaged over the 30 images. The results show that using two neural-network classifiers on the input textural and spectral characteristics of selected multispectral bands, we obtained FARs of approximately 3%. Using polarization-resolved images only, we obtained FARs of 1.15%. Fusing the best classifier output with the polarization-resolved images, we obtained FARs as low as 0.023%. This result has shown the large improvement gained in the sensor fusion. Also, an improvement is shown by comparing these results with those reported in an existing commercial system     

Multisensor data fusion for physical activity assessment

This paper presents a sensor fusion method for assessing physical activity (PA) of human subjects, based on support vector machines (SVMs). Specifically, acceleration and ventilation measured by a wearable multisensor device on 50 test subjects performing 13 types of activities of varying intensities are analyzed, from which activity type and energy expenditure are derived. The results show that the method correctly recognized the 13 activity types 88.1% of the time, which is 12.3% higher than using a hip accelerometer alone. Also, the method predicted energy expenditure with a root mean square error of 0.42 METs, 22.2% lower than using a hip accelerometer alone. Furthermore, the fusion method was effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition, especially when data from the ventilation sensor were added to the fusion model. These results demonstrate that the multisensor fusion technique presented is more effective in identifying activity type and energy expenditure than the traditional accelerometer-alone-based methods.     

Multisensor fusion in the frame of evidence theory for landmines detection

In the frame of humanitarian antipersonnel mines detection, a multisensor fusion method using the Dempster-Shafer evidence theory is presented. The multisensor system consists of two sensors-a ground penetrating radar (GPR) and a metal detector (MD). For each sensor, a new features extraction method is presented. The method for the GPR is mainly based on wavelets and contours extraction. First simulations on a limited set of data show that an improvement in detection and false alarms rejection, for the GPR as a standalone sensor, could be obtained. The MD features extraction method is mainly based on contours extraction. All of these features are then fused with the GPR ones in some specific cases in order to determine a new feature. From these results, belief functions, as defined in the evidence theory, are then determined and combined thanks to the orthogonal sum. First results in terms of detection and false alarm rates are presented for a limited set of real data and a comparison is made between the two cases: with or without multisensor fusion.     

Multisensor towed array detection system for UXO detection

The multisensor towed array detection system (MTADS) was designed to be an efficient, sensitive tool for the detection and characterization of buried unexploded ordnance. It comprises arrays of total-field magnetometers and time-domain electromagnetic induction (EMI) sensors, associated navigation and data acquisition hardware, and a custom data analysis system. The MTADS has conducted eleven demonstrations and surveys. The system has shown the ability to detect ordnance at its likely self-penetration depths with a probability of detection of 0.95 or better The model-derived positions and depths of the detected ordnance items are generally well within the physical size of the targets, making remediation much quicker and less costly than with standard techniques. Data sets corresponding to many of the MTADS surveys are available to others in the field     

多传感器和数据融合(续)

文中介绍了多传感器集成及数据融合的概念、优点、结构、方法和应用,列举了传感器四种不同集成度的特点。数据融合把来自不同传感器或其它信息源的数据加以综合、相关、互联,以便提高定位和特征估计的精度。在数据融合过程中建模包括信号模型、噪声模型、变换器模型、数据变换模型以及融合模型。数据融合模型包括事例的方法和结构,文章介绍了集成式、分布式和混合式融合结构,并进行了比较。此外,还介绍了国外一些数据融合的试验     

多传感器和数据融合(一)

文中介绍了多传感器集成及数据融合的概念、优点、结构、方法和应用,列举了传感器四种不同集成度的特点。数据融合把来自不同传感器的、或其它信息源的数据加以综合、相关、互联,以便提高定位和特征估计的精度。在数据融合过程中建模包括信号模型、噪声模型、变换器模型、数据变换模型以及融合模型。数据融合模型包括融合的方法和结构,文章介绍了集成式、分布式和混合式融合结构,并对它们进行了比较。此外,还介绍了国外一些数据融合的试验系统,商业软件和应用的例子。     

Multisensor integration and fusion model that uses a fuzzyinference system

An intelligent multisensor integration and fusion model that uses fuzzy logic is developed. Measurement data from different types of sensors with different resolutions are integrated and fused based on the confidence in them derived from information not usually used in data fusion, such as operating temperature, frequency range, fatigue cycles, etc. These are fed as additional inputs to a fuzzy inference system (FIS) that has predefined membership functions for each of these variables. The output of the FIS are weights that are assigned to the different sensor measurement data that reflect the confidence in the sensor's behavior and performance. A modular approach is adopted. It allows adding or deleting a sensor, along with its fuzzy logic controller (FLC), anytime without affecting the entire data fusion system. This paper presents a preliminary model that fuses the data from three different types of sensors that monitor the strain at a single location in a cantilever beam. This will be later extended to sensors that will be fixed at different locations on the same beam. The results from the proposed work are a stepping stone toward the development of generic autonomous sensor models that are capable of data interpretation, self-calibration, data fusion from other sources, and even learning so as to improve their performance with time. This work is aimed at the development of smart structural health monitoring systems, but has applications in diverse fields such as robotics, controls, target tracking, and biomedical imaging     

Computer modeling of ventricular rhythm during atrial fibrillation and ventricular pacing

We propose a unified atrial fibrillation (AF)-ventricular pacing (VP) (AF-VP) model to demonstrate the effects of VP on the ventricular rhythm during atrial fibrillation AF. In this model, the AV junction (AVJ) is treated as a lumped structure characterized by refractoriness and automaticity. Bombarded by random AF impulses, the AVJ can also be invaded by the VP-induced retrograde wave. The model includes bidirectional conduction delays in the AVJ and ventricle. Both refractory period and conduction delay of the AVJ are dependent upon its recovery time. The electrotonic modulation by blocked impulses is also considered in the model. Our simulations show that, with proper parameter settings, the present model can account for most principal statistical properties of the RR intervals during AF. We further demonstrate that the AV conduction property and the ventricular rate in AF depend on both AF rate and the degree of electrotonic modulation in the AVJ. Finally, we show that multilevel interactions between AF and VP can generate various patterns of ventricular rhythm that are consistent with previous experimental observations.     

Multi-sensor fusion method for roadheader pose detection

Pose detection of roadheader has become an important topic in the field of mine automation. This paper presents a method of multi sensor data fusion for the detection of roadheader fuselage pose. The pose detection system consists of a cross laser transmitter, several photoelectric sensors and an inclination sensor. The laser transmitter is used to create reference coordinate frame. The photoelectric sensors and inclination sensor are used to acquire data for the calculation of roadheader pose. The spatial algorithm for the pose parameters of roadheader is derived by fusing the measurement data of photoelectric sensors and inclination sensor. A test platform is built to verify the performance of the pose estimation system. The experimental results show that by using multi-sensor data fusion method, the pose estimation system has higher measurement accuracy and can meet the requirements of engineering application.     

Inexpensive fusion methods for enhancing feature detection

Recent successful approaches to high-level feature detection in image and video data have treated the problem as a pattern classification task. These typically leverage the techniques learned from statistical machine learning, coupled with ensemble architectures that create multiple feature detection models. Once created, co-occurrence between learned features can be captured to further boost performance. At multiple stages throughout these frameworks, various pieces of evidence can be fused together in order to boost performance. These approaches whilst very successful are computationally expensive, and depending on the task, require the use of significant computational resources. In this paper we propose two fusion methods that aim to combine the output of an initial basic statistical machine learning approach with a lower-quality information source, in order to gain diversity in the classified results whilst requiring only modest computing resources. Our approaches, validated experimentally on TRECVid data, are designed to be complementary to existing frameworks and can be regarded as possible replacements for the more computationally expensive combination strategies used elsewhere.     

Robust data fusion for multisensor detection systems

Minimax robust data fusion schemes for multisensor detection systems with discrete-time observations characterized by statistical uncertainty are developed and analyzed. Block, sequential, and serial fusion rules are considered. The performance measures used, and made robust with respect to the uncertainties, include the error probabilities of the hypothesis testing problem in the block fusion case and the error probabilities and expected numbers of samples or sensors in the sequential and serial fusion cases. For different sensor observation statistics, the minimax robust fusion rules are derived for two asymptotic cases of interest: when the number of sensors is large and when the number of times the fusion center collects the local (sensor) decisions is large. Moreover, for the case of identical sensor observation statistics and a large number of sensors, it is shown that there is no loss in optimality, if local tests using likelihood ratios and equal thresholds are used in the sequential fusion rule. In all situations, the robust decision rules at the sensors and the fusion center are shown to make use of likelihood ratios and thresholds that depend on the least-favorable probability distributions of the uncertainty class describing the statistics of sensor observations     

模糊聚合及遗传算法在多传感器数据融合中的应用

文中给出了多传感器数据融合的一个模糊－－遗传算法，数据融合过程中的推理由模糊聚合函数完成，模糊聚合比传统的集合率中的并和交操作能更地地模拟答的量。模糊聚合函数的连接参数由遗传算法确定。这２个算法的优点是在信息源的可靠性１信息的 余度／互补性以及进行融合的分级结构不确定的情况下能够色近了倨的方法对信息进行融合。     

CNN spatiotemporal features and fusion for surveillance video forgery detection

Surveillance cameras are widely used to provide protection and security; also their videos are used as strong evidences in the courts. Through the availability of video editing tools, it has become easy to distort these evidences. Sometimes, to hide the traces of forgery, some post-processing operations are performed after editing. Hence, the authenticity and integrity of surveillance videos have become urgent to scientifically validate. In this paper, we propose inter-frame forgeries (frame deletion, frame insertion, and frame duplication) detection system using 2D convolution neural network (2D-CNN) of spatiotemporal information and fusion for deep automatically feature extraction; Gaussian RBF multi-class support vector machine (RBF-MSVM) is used for classification process. The experimental results show that the efficiency of the proposed system for detecting all inter-frame forgeries, even when the forged videos have undergone additional post-processing operations such as Gaussian noise, Gaussian blurring, brightness modifications and compression.     

基于认知无线电系统合作检测的数据融合研究

从多传感器数据融合角度研究认知无线电系统合作感知问题.对于特定的在线用户规模,存在一个最佳的参与部分融合用户数,可以固定选择每次检测的融和用户数使系统平均检测概率最大.在此基础上提出一种基于测量的融合方法,根据每次检测的接收信号状况动态调整参与融合的用户数.仿真表明,这种方法可以使系统检测性能在60%以上的检测中相对于固定融合用户数的方法有所提高.     

基于图像融合的运动前景检测方法

提出了一种有效的运动前景检测方法。该方法根据图像融合思想,将背景帧与监控视频的当前帧在R,G和B颜色通道分别进行融合,形成包含背景帧和当前帧视觉信息的单一融合图像。之后根据背景区域与前景运动目标在饱和度上存在较大差异的现象,使用大津算法分割融合图像的饱和度分量图,形成运动前景二值图。经形态学处理后,形成了目标区域较完整、背景干净的运动前景检测图。实验结果显示,该算法具有较好的前景检测性能,解决了背景减法过分依赖背景帧的缺陷。     

一种双/多波段红外目标融合检测方法

针对双/多波段红外目标检测问题,提出了一种基于自适应加权投票融合准则的红外目标融合检测方法。该方法首先对双/多波段红外传感器进行图像配准,然后对单传感器红外图像进行处理,得到单传感器目标检测结果,最后使用提出的自适应加权投票融合准则,对单传感器目标检测结果进行融合,得到最终判决。实验结果显示,该算法能在较大程度上降低目标检测过程中的不确定性,从而提高了系统的检测性能,同单波段检测结果和其他的融合结果相比,该方法能有效地降低漏警概率和虚警概率;并且该方法易于实现,并在实际工程中得到了应用。     

一种心电监测和房颤预警系统的研究

随着社会进步和经济的发展,在人们生活水平提高的同时,各种心血管疾病成为人类健康的隐形杀手。本文提出了一种基于嵌入式架构的便携式心电监测和房颤预警系统,实现了受试者心电信号实时采集监测的功能,采集数据通过无线网络传输至云端进行存储,监测者可以远程获取云上存储的心电监测数据,下载并采用基于主分量解析分析去获取受试者心电房颤的特征信息,准确判断受试者是否出现房颤的情况,从而实现受试者的心脏健康状况,监测结果可以在监测设备显示屏以及远程安卓手机的APP程序中进行显示。本研究所开发的系统具有成本低廉、操作简便、运行可靠等优势。     

多传感器数据融合发展评述及展望

结合近年来国内外的研究成果,回顾数据融合研究的发展历程,介绍了多传感器数据融合的概念和模型,探讨了数据融合的多层式体系结构,对数据融合理论的发展和研究现状进行了分析,对未来研究方向进行了展望。     

基于时空域融合滤波的红外运动小目标检测算法

针对红外警戒与跟踪系统中的实时弱小运动目标检测问题．提出了一种基于时空域融合滤波的小目标检测算法。算法在空域上利用形态学Tophat滤波抑制背景增强目标．在时域上通过改进的帧间差分方法增强运动目标,时空域处理结果融合分割后,根据目标运动的连续性和规则性．利用相邻帧中可能目标点之间的位置关系判别目标。算法全面考虑到了运动小目标在时域与空域方面的特性,时空域融合增强后可大大提高目标信噪比。通过实际录取的云层背景飞机目标红外数据检测表明,时空域融合滤波方法能更有效地从复杂背景中检测低信噪比运动小目标,减小虚警率．抗噪声干扰能力强。算法易于硬件实现,能够有效地应用于红外搜索与跟踪系统的实时目标检测中。     

用于激光聚变的X射线条纹相机阴极检测系统

研制了一套X射线条纹相机光电阴极检测系统,用于激光惯性约束聚变中阴极的快速标定和检测。通过三位一体的条纹变像管设计,条纹变像管电子光学系统的优化,真空室、控制系统的制备,系统的装调、集成和测试,研制了光电阴极检测系统。组建了阴极系统静态测试平台,标定了其静态特性,测试结果显示:3条条纹像中心的偏移率在狭缝方向为2.8%,在垂直于狭缝方向为6.6%,平均放大倍率1.29,误差在0.8%,边缘空间分辨率大于10 lp/mm。该系统可以满足激光聚变诊断研究对于X射线条纹相机光电阴极的检测需求。