具有多传感器的CPS系统的攻击检测

信息物理系统（cyber-physical systems，简称CPS）是基于环境感知实现计算、通信与物理元素紧密结合的下一代智能系统，广泛应用于安全攸关的系统和工业控制等领域.信息技术与物理世界的相互作用使得CPS容易受到各种恶意攻击，从而破坏其安全性.主要研究存在瞬态故障的CPS中传感器的攻击检测问题.考虑具有多个传感器测量相同物理变量的系统，其中一些传感器可能受到恶意攻击并提供错误的测量.此外，使用抽象传感器模型，每个传感器为控制器提供一个真实值的可能间隔.已有的用于检测传感器被恶意攻击的方法是保守的.当专业攻击者在一段时间内轻微地或不频繁地操纵传感器的输出时，现有方法很难捕获到攻击，如隐身攻击.为了解决这个问题，设计了一种基于融合间隔和历史测量的传感器攻击检测方法.该方法首先为不同的传感器构建不同的故障模型，使用系统动力学方程把历史测量融入到攻击检测方法中，从不同的方面分析传感器的测量.另外，利用历史测量和融合间隔解决了两个传感器的测量相交时是否存在故障的问题.该方法的核心思想是利用传感器之间的成对不一致关系检测和识别攻击.从EV3地面车辆上获得真实的测量数据来验证算法的性能.实验结果表明，所提出的方法优于现有方法，对各种攻击类型都有较好的检测和识别性能，特别是对于隐身攻击，检测率和识别率大约提高了90%以上.     

基于DSmT理论的多视角融合目标检测识别

现有无人车在目标检测中大多依靠单一检测视角进行多传感器数据融合,受传感器检测范围的局限,难以大幅提高准确率,且对融合过程中的类别判定的高冲突情况处理较少．针对以上问题,本文基于多假设思想提出了多视角检测结果的聚类合并方法,并基于DSmT（Dezert-Samarandache theory）和时序信息,改进了冲突分配准则,降低了目标检测的漏检率与误检率．首先利用图像检测算法检测图像中的有效目标,将激光雷达的目标检测结果投影在图像平面上,通过交并比关系构建2种传感器检测结果之间的关联概率矩阵,基于多假设思想实现聚类合并,获取单帧融合检测结果．针对融合过程中可能出现的类别判定冲突情况,利用DSmT融合识别置信度,并结合时序信息对冲突重新分配,获取目标类别的准确识别结果．最后,通过实车实验对算法的有效性进行了验证．     

Building 3D visual maps of interior space with a new hierarchical sensor fusion architecture

It is now generally recognized that sensor-fusion is the best approach to the accurate construction of environment maps by a sensor-equipped mobile robot. Typically, range data collected with a range sensor is combined with the reflectance data obtained from one or more cameras mounted on the robot.In much of the past work on sensor fusion in hierarchical approaches to map construction, the fusion was carried out only at the lowest level of the hierarchy. As a result, in those approaches, only the fused data was made available to the higher levels in the hierarchy. This implied that any errors caused by sensor fusion would propagate upwards into the higher level representations of an interior map. Our work, on the other hand, checks for consistency between the data elements produced by the different sensors at all levels of the hierarchy. This consistency checking is carried out with the help of an interval-based representation of uncertainties in the sensor data.In addition to demonstrating that our approach to the fusion of range and image data results in dense 3D maps of the interior space, we also provide validation of our overall framework by presenting a set of loop closure results. These results demonstrate that our overall errors in the maps remain small (within 0.91% of the distance traveled for map construction) even when the robot has to traverse over large loops inside a building.     

LSFR算法在多传感器分布式检测中的优化研究

在多传感器分布式检测系统中,常规融合规则算法要求传感器误差概率已知,且系统中传感器和融合中心同时优化存在一定困难.提出最小二乘融合规则(LSFR)算法,算法不依赖噪声环境稳定性以及传感器的虚警概率与检测概率,融合中心根据各个传感器的硬决策,得到全局的硬决策,并在传感器和融合中心处理达到最优时,获得最佳全局性能.仿真结果表明:对比似然比融合决策算法与Neyman Pearson融合规则(NPFR)算法,LSFR算法全局检测概率显著提高,且在不同数量规模传感器和更多类型的分布式检测系统中具有较好兼容性.     

MRNN:一种新的基于改进型递归神经网络的WSN动态建模方法:应用于故障检测

提出了一种适用于无线传感器网络WSN的故障检测方法,该方法运用改进的递归神经网络MRNN为WSN的节点、节点的动态特性以及节点间的关系建立相关模型,对WSN节点进行识别和故障检测。MRNN的输入选择建模节点的先前输出值及其邻居节点的当前及先前输出值,模型基于一种新的改进的反向传播型神经网络,该神经网络的输入以及传感器网络的拓扑结构基于通用的非线性传感器模型。仿真实验将MRNN方法与卡尔曼滤波法进行了全面的比较。实验表明,MRNN在置信因子较小的情况下与卡尔曼滤波方法相比有较高的故障检测精度。     

基于区间预测模型的污水处理厂传感器故障检测

污水处理厂配备许多传感器用于监测出水水质。传感器的正常工作与否对保证出水水质至关重要。给出了一种污水处理出水变量传感器故障检测方法。该方法根据入水和出水数据,采用径向基函数神经网络构造出水变量预测模型;使用参数线性集员辨识算法得到网络输出权值的集合描述,从而使预测模型能够给出出水变量的置信区间;以此置信区间为基础获得传感器的故障检测策略。由于置信区间描述了出水变量的存在范围,当传感器测量值超出置信区间,则可推断传感器发生故障。此外,在设计传感器故障检测策略时还考虑了污水处理过程异常的影响。实验结果证实所提方法的有效性。     

基于深度学习算法的无线传感器异常数据检测

提出了基于深度学习的异常数据检测的方法,精准检测到无线传感器异常数据并直观展现检测结果。基于无线传感器网络模型分簇原理,通过异常数据驱动的簇内数据融合机制,去除无线传感器网络中的无效数据,获取无线传感器网络有效数据融合结果。构建了具有4层隐含层的深度卷积神经网络,将预处理后的无线传感器网络数据作为模型输入,通过隐含层完成数据特征提取和映射后,由输出层输出异常数据检测结果。实验证明：该方法可有效融合不同类型数据,且网络节点平均能耗较低;包含4层隐含层的深度卷积神经网络平均分类精度高达98.44%,1000次迭代后隐含层的训练损失均趋于0,可实现无线传感器异常数据实时、直观、准确检测。     

Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions

Activity detection and classification using different sensor modalities have emerged as revolutionary technology for real-time and autonomous monitoring in behaviour analysis, ambient assisted living, activity of daily living (ADL), elderly care, rehabilitations, entertainments and surveillance in smart home environments. Wearable devices, smart-phones and ambient environments devices are equipped with variety of sensors such as accelerometers, gyroscopes, magnetometer, heart rate, pressure and wearable camera for activity detection and monitoring. These sensors are pre-processed and different feature sets such as time domain, frequency domain, wavelet transform are extracted and transform using machine learning algorithm for human activity classification and monitoring. Recently, deep learning algorithms for automatic feature representation have also been proposed to lessen the burden of reliance on handcrafted features and to increase performance accuracy. Initially, one set of sensor data, features or classifiers were used for activity recognition applications. However, there are new trends on the implementation of fusion strategies to combine sensors data, features and classifiers to provide diversity, offer higher generalization, and tackle challenging issues. For instances, combination of inertial sensors provide mechanism to differentiate activity of similar patterns and accurate posture identification while other multimodal sensor data are used for energy expenditure estimations, object localizations in smart homes and health status monitoring. Hence, the focus of this review is to provide in-depth and comprehensive analysis of data fusion and multiple classifier systems techniques for human activity recognition with emphasis on mobile and wearable devices. First, data fusion methods and modalities were presented and also feature fusion, including deep learning fusion for human activity recognition were critically analysed, and their applications, strengths and issues were identified. Furthermore, the review presents different multiple classifier system design and fusion methods that were recently proposed in literature. Finally, open research problems that require further research and improvements are identified and discussed.     

基于优化深度置信网络的多传感器水质监测研究

为了满足多种水环境的大范围、精准监测需求,提出了基于优化深度置信网络的多传感器水质监测方法。设置水质监测标准,作为水质等级的判定条件。优化设计水体温度、PH值、溶解氧、浊度等传感器设备,利用优化深度置信网络选择多传感器的安装位置。利用多传感器采集水环境数据并完成融合处理,通过多个水质监测指标的计算以及与设置标准的比对,得出多传感器水质监测的可视化输出结果。通过性能测试实验得出结论：优化设计方法的水质监测范围为2041.79平方千米,浊度、pH值、溶解氧和氨氮浓度指标的监测误差分别为0.005FTU、0.07、0.05mg/L和0.007mg/L,均低于传统方法,且满足预设条件。     

基于贝叶斯数据融合的轨道车辆pt100温度检测系统设计

Pt100温度传感器是轨道车辆温度数据实时监测的常用传感器,其性能的好坏影响着人们对轨道车辆运行状态的判断.为了准确判断出存在故障的传感器,检测系统应包含精准的信号采集系统和有效的数据融合处理方法.首先对轨道车辆Pt100温度传感器信号采集系统的放大电路、A/D转换电路等进行设计,采集系统采用分段非线性多项式拟合算法,得到不同温度区间上的标度变换表达式;将一种基于贝叶斯估计算法的多传感器测量数据融合方法应用于采集信号的处理,判断轨道车辆Pt100温度传感器是否存在故障.研究结果表明,采用上述过程测量温度精准,融合误差小,能够有效筛选出发生故障的传感器.     

On the performance of distributed Neyman-Pearson detection systems

The performance of a distributed Neyman-Pearson detection system is considered. We assume that the decision rules of the sensors are given and that decisions from different sensors are mutually independent conditioned on both hypotheses. The purpose of decision fusion is to improve the performance of the overall system, and we are interested to know under what conditions can a better performance be achieved at fusion center, and under what conditions cannot. We assume that the probabilities of detection and false alarm of the sensors can be different. By comparing the probability of detection at fusion center with that of each of the sensors, with the probability of false alarm at fusion center constrained equal to that of the sensor, we give conditions for a better performance to be achieved at fusion center     

基于多传感器信息融合的移动机器人快速精确自定位

通过分析全向视觉、电子罗盘和里程计等传感器的感知模型,设计并实现了一种给定环境模型下移动机器人全局自定位算法.该算法利用蒙特卡罗粒子滤波,融合多个传感器在不同观测点获取的观测数据完成机器人自定位.与传统的、采用单一传感器自定位的方法相比,它把多个同质或异质传感器所提供的不完整测量及相关联数据库中的信息加以综合,降低单个...     

Space and time sensor fusion using an active camera for mobile robot navigation

We propose a sensor-fusion technique where the data sets for previous moments are properly transformed and fused into the current data sets to allow accurate measurements, such as the distance to an obstacle or the location of the service robot itself. In conventional fusion schemes, measurements are dependent on the current data sets. As a result, more sensors are required to measure a certain physical parameter or to improve the accuracy of a measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequences of the data sets are stored and utilized to improve the measurements. The theoretical basis is illustrated by examples, and the effectiveness is proved through simulations. Finally, the new space and time sensor fusion (STSF) scheme is applied to the control of a mobile robot in an unstructured environment and a structured environment.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

A new multiple decisions fusion rule for targets detection in multiple sensors distributed detection systems with data fusion

Currently, multiple sensors distributed detection systems with data fusion are used extensively in both civilian and military applications. The optimality of most detection fusion rules implemented in these systems relies on the knowledge of probability distributions for all distributed sensors. The overall detection performance of the central processor is often worse than expected due to instabilities of the sensors probability density functions. This paper proposes a new multiple decisions fusion rule for targets detection in distributed multiple sensor systems with data fusion. Unlike the published studies, in which the overall decision is based on single binary decision from each individual sensor and requires the knowledge of the sensors probability distributions, the proposed fusion method derives the overall decision based on multiple decisions from each individual sensor assuming that the probability distributions are not known. Therefore, the proposed fusion rule is insensitive to instabilities of the sensors probability distributions. The proposed multiple decisions fusion rule is derived and its overall performance is evaluated. Comparisons with the performance of single sensor, optimum hard detection, optimum centralized detection, and a multiple thresholds decision fusion, are also provided. The results show that the proposed multiple decisions fusion rule has higher performance than the optimum hard detection and the multiple thresholds detection systems. Thus it reduces the loss in performance between the optimum centralized detection and the optimum hard detection systems. Extension of the proposed method to the case of target detection when some probability density functions are known and applications to binary communication systems are also addressed.     

Multi-Camera Tracking with Adaptive Resource Allocation

Sensor fusion for object tracking is attractive since the integration of multiple sensors and/or algorithms with different characteristics can improve performance. However, there exist several critical limitations to sensor fusion techniques: (1) the measurement cost increases typically as many times as the number of sensors, (2) it is not straightforward to measure the confidence of each source and give it a proper weight for state estimation, and (3) there is no principled dynamic resource allocation algorithm for better performance and efficiency. We describe a method to fuse information from multiple sensors and estimate the current tracker state by using a mixture of sequential Bayesian filters (e.g., particle filter)—one filter for each sensor, where each filter makes a different level of contribution to estimate the combined posterior in a reliable manner. In this framework, multiple sensors interact to determine an appropriate sensor for each particle dynamically; each particle is allocated to only one of the sensors for measurement and a different number of particles is assigned to each sensor. The level of the contribution of each sensor changes dynamically based on its prior information and relative measurement confidence. We apply this technique to visual tracking with multiple cameras, and demonstrate its effectiveness through tracking results in videos.     

Optimal interval estimation fusion based on sensor interval estimates with confidence degrees

The interval estimation fusion method based on sensor interval estimates and their confidence degrees is developed. When sensor estimates are independent of each other, a combination rule to merge sensor estimates and their confidence degrees is proposed. Moreover, two optimization criteria: minimizing interval length with an allowable minimum confidence degree, or maximizing confidence degree with an allowable maximum interval length are suggested. In terms of the two criteria, an optimal interval estimation fusion can be obtained based on the combined intervals and their confidence degrees. Then we can extend the results on the combined interval outputs and their confidence degrees to obtain a conditional combination rule and the corresponding optimal fault-tolerant interval estimation fusion in terms of the two criteria. It is easy to see that Marzullo's fault-tolerant interval estimation fusion [Marzullo, (1990). Tolerating failures of continuous-valued sensors. ACM Transactions on Computer System, 8(4), 284-304] is a special case of our method.     

风向传感器现场自动核查仪系统设计

针对格雷码式风向传感器的核查需求,尤其针对隐性故障较难排查的问题,设计了风向传感器现场自动核查仪系统。通过电源模块设计,实现电池供电,传感器直接接入即可检测,并且无需额外给传感器供电;通过格雷码点位检测(格雷码的每个点位都有高低电平变化、相邻2组的格雷码只变化1个点位和格雷码不同点位存在不同的高低电平交替出现的规律)和风向值检测(0°检测点读数正常、风向值依次变化规律和所有风向值都应出现)等方法,操作方法更简便且可更全面地排查故障。本系统通过了模拟故障测试和省市县相关气象部门的业务试用。结果表明,本系统便携易用,检测周期缩短到5秒,检测结果的输出清晰准确,能较好地满足风向传感器现场核查的业务要求。     

面向目标侦测的非配合式无线传感器网络研究

针对战场目标侦测需求,在满足无线传感器网络低成本、低功耗约束条件的前提下,选择了传感器组合,设计实现了非配合式无线传感器网络硬件平台,采用红外传感器作为"唤醒传感器"实现节点休眠唤醒功能,同时检测人员与车辆,磁阻传感器负责铁磁物质检测,声音传感器实现车辆与人员的声音信号特征检测,利用简单实用的数据处理方法对各传感器信号...     

基于长定子齿槽的磁浮列车测速

磁浮列车测速定位传感器在实际运行时,会受到列车震动和长定子轨道的接缝等影响,并且轨道接缝的尺寸也不相同,使得位置传感器在通过不同轨道接缝的时候产生不同的信号畸变,导致牵引设备过流或过压保护甚至烧毁。为了能够适应不同的轨道接缝的影响,本测速定位系统采用了两路相对位置传感器,应用自适应滤波和周期预测的方法,设计了一种自适应预测滤波器,通过周期预测方法将预测值与实际观测值进行比较,选择与预测值相近的传感器信号。通过提取两路传感器中的正确信号,实现了传感器的冗余处理,列车实际运行实验验证所提出方法的正确性。     

基于模糊逻辑应用于反馈结构的多传感器航迹融合的研究

在分布式多传感器信息融合系统中,反馈结构可用来提高局部节点的跟踪性能,进而提高全局航迹的融合精度.模糊逻辑应用于对多个航迹的位置偏差、速度偏差和加速度偏差进行模糊化、模糊逻辑推理及去模糊等,提出了关于模糊逻辑应用于反馈结构的多传感器航迹融合的方法.