Coordinated dispatching of proximity sensors for the surveillance of manoeuvring targets

The surveillance of a manoeuvring target with multiple sensors in a coordinated manner requires a method for selecting and positioning groups of sensors in real time. Herein, the principles of dispatching, as used for the effective operation of service vehicles, are considered. The object trajectory is first discretized into a number of demand instants (data acquisition times), to which groups of sensors are assigned, respectively. Heuristic rules are used to determine the composition of each sensor group by evaluating the potential contribution of each sensor. In the case of dynamic sensors, the position of each sensor with respect to the target is also specified. Our proposed approach aims to improve the quality of the surveillance data in three ways: (1) The assigned sensors are manoeuvred into “optimal” sensing positions, (2) the uncertainty of the measured data is mitigated through sensor fusion, and (3) the poses of the unassigned sensors are adjusted to ensure that the surveillance system can react to future object manoeuvres. If a priori target trajectory information is available, the system performance may be further improved by optimizing the initial pose of each sensor off-line. The advantages of dispatching dynamic sensors over similar static-sensor systems are demonstrated through comprehensive simulations.     

Combining Intelligent Techniques for Sensor Fusion

Mobile robots rely on sensor data to build a representation of their environment. However, sensors usually provide incomplete, inconsistent or inaccurate information. Sensor fusion has been successfully employed to enhance the accuracy of sensor measures. This work proposes and investigates the use of Artificial Intelligence techniques for sensor fusion. Its main goal is to improve the accuracy and reliability of the distance measure between a robot and an object in its work environment, based on measures obtained from different sensors. Several Machine Learning algorithms are investigated to fuse the sensors data. The best model generated by each algorithm is called estimator. It is shown that the employment of estimators based on Artificial Intelligence can improve significantly the performance achieved by each sensor alone. The Machine Learning algorithms employed have different characteristics, causing the estimators to have different behaviors in different situations. Aiming to achieve an even more accurate and reliable behavior, the estimators are combined in committees. The results obtained suggest that this combination can further improve the reliability and accuracy of the distances measured by the individual sensors and estimators used for sensor fusion.     

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...     

自动驾驶3D目标检测研究综述

精确实时地进行目标检测是自动驾驶车辆能够准确感知周围复杂环境的重要功能之一,如何对周围物体的尺寸、距离、位置、姿态等3D信息进行精准判断是自动驾驶3D目标检测的经典难题.服务于自动驾驶的3D目标检测已成为近年来炙手可热的研究领域,鉴于此,对该领域主要研究进展进行综述.首先,介绍自动驾驶感知周围环境各相关传感器的特点;其次,介绍3D目标检测算法并按照传感器获取数据类型将其分为:基于单目/立体图像的算法、基于点云的算法以及图像与点云融合的算法;然后,对每类3D目标检测的经典算法以及改进算法进行详细综述、分析、比较,梳理了当前主流自动驾驶数据集及其3D目标检测算法的评估标准,并对现有文献广泛采用的KITTI和NuScenes数据集实验结果进行对比及分析,归纳了现有算法存在的难点和问题;最后,提出自动驾驶3D目标检测在数据处理、特征提取策略、多传感器融合和数据集分布问题方面可能遇到的机遇及挑战,并对全文进行总结及展望.     

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．     

巡逻式多电子哨兵目标识别的数据融合方法

对巡逻式电子哨兵目标观测在数据层的精确定位方法,以及决策层目标精确识别的数据融合问题进行研究。将个体观测的局部坐标系的目标位置转换到全局坐标系中,利用卡尔曼方法融合数据层信息。决策层目标识别的信息融合采用改进的多层次D-S证据论融合方法,将单个哨兵各异类传感器信息融合后再进行多个电子哨兵识别结果的信息融合。实验结果表明,融合后的数据稳定性和准确性都能得到提高,目标识别的正确率提高了20%。     

基于变异系数的模糊传感器数据融合方法

在多传感器信息融合过程中,有时要利用多传感器对某一对象进行不同方面观测,而数据融合的目的是要对研究对象进行类型识别。根据统计分析理论,利用变异系数,给出了一种新的模糊传感器数据的融合算法。该方法不需要假设总体的分布类型,不需要定义受主观因素影响的关系矩阵,可以充分利用测量数据,避免丢失重要的极端数据,使结论更符合实际。仿真实验表明:算法有效,非常简单。     

Complementing visual tracking of moving targets by fusion of tactile sensing

Robot control in uncertain and dynamic environments can be greatly improved using sensor-based control. Vision is a versatile low-cost sensory modality, but low sample rate, high sensor delay and uncertain measurements limit its usability, especially in strongly dynamic environments. Vision can be used to estimate a 6-DOF pose of an object by model-based pose-estimation methods, but the estimate is typically not accurate along all degrees of freedom. Force is a complementary sensory modality allowing accurate measurements of local object shape when a tooltip is in contact with the object. In multimodal sensor fusion, several sensors measuring different modalities are combined together to give a more accurate estimate of the environment. As force and vision are fundamentally different sensory modalities not sharing a common representation, combining the information from these sensors is not straightforward. We show that the fusion of tactile and visual measurements enables to estimate the pose of a moving target at high rate and accuracy. Making assumptions of the object shape and carefully modeling the uncertainties of the sensors, the measurements can be fused together in an extended Kalman filter. Experimental results show greatly improved pose estimates with the proposed sensor fusion.     

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.     

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.     

煤矿多传感器混沌时序数据融合预测

针对单传感器煤矿数据预测存在的片面性问题,提出将信息融合技术与相空间重构技术相结合的多传感器煤矿数据的预测模型。对井下多种传感器,包括瓦斯浓度、风速、温度传感器,进行融合预测。以多类传感器时序数据为研究对象,首先利用信息融合的方法分别对各类传感器数据依次进行数据层融合、特征层融合;然后采用关联积分方法对两级融合之后的传感器数据分别确定相重构的时间延迟τ和嵌入维数m两个参数;最后结合多变量相空间重构技术,将各类传感器数据融合重构相空间,运用基于K-Means聚类的加权一阶局域法构建多传感器数据的预测模型。数据来源于山西省阳泉煤矿,采集了近20G数据,以瓦斯浓度、风速、温度三种传感器数据进行实验,实验结果表明：对于特征层的融合,每15分钟时间段内的数据经融合后可有效作为衡量这段时间内的特征,经过预测模型计算后,与时间段为5分钟、10分钟、20分钟相比较误差达到最小ESS=0.003,较目前的最小误差值0.05,误差大大下降,故融合预测效果较好,可以较准确地预测未来15分钟后的传感器数据,可有充足时间进一步为井下的安全评估提供决策依据。     

基于Android的多传感器信息融合技术应用

介绍基于Android的多传感器信息融合技术在气溶胶自动化检测中的应用。多个传感器采用统一接口方式组网,每一个独立传感器采集的数据通过蓝牙传输到网内的Android处理终端进行融合和分析。通过对5种传感器进行组网采集数据,结果证明多传感器信息融合技术的应用增强了信息处理系统的适应性,实现了传感器之间的数据互补,提高了系...     

一种基于模糊融合的遥感图像目标识别方法

数据融合利用多传感器的信息,克服了单一传感器信息不完整、不精确、不确定的缺点,因此广泛应用于目标识别中,该文提出了一种基于模糊融合的遥感图像目标识别的新方法。首先在单源图像上提取可疑目标,然后根据目标在不同类型图像上的成像特点,选择合适的目标特征,充分考虑到各特征的重要程度,把模糊隶属度函数和模糊密度结合起来,最后利用特征层模糊融合对目标的身份进行判定。此方法应用在实际目标的识别中,取得了很好的效果。     

分布式多传感信息融合系统的异步航迹关联方法

在分布式多传感信息融合系统中,由于每个局部传感器的采样频率不同以及具有不同的通信延迟,导致来自不同传感器的局部航迹往往是异步的.针对此问题,提出了一种异步航迹关联方法.该方法首先基于最小二乘法实现单传感器的时域融合,从而将多传感异步航迹同步化.接下来,将多传感多目标航迹关联问题转化为在网络中搜索总费用最小的多个互不相交的路径问题,从而获得相应于每个目标的各个传感器的局部航迹组合.仿真试验表明,算法可以有效地解决异步航迹的关联问题,且具有较高的关联成功率.     

Track Compensation Algorithm Using Free Space Information with Occupancy Grid Map

Over the past few years, numerous technologies have emerged to enable safe and convenient driving. However, there still exist various problems autonomous vehicles should overcome. Precise detection and perception of surrounding environments are the essential foundations to overcome them. Consequently, many sensor fusion algorithms have been developed to handle more complex situations, with sensor manufacturers also making strenuous efforts to enhance sensor performance. Although Light Detection And Ranging(LiDAR) sensor generally outperforms other sensor types, they remain prohibitively expensive from car manufacturing companies perspective. Therefore, camera and radar sensors have been enhanced, and are starting to provide free space information, similar to LiDAR sensor data and somewhat different from target information they have previously provided. The aim of this paper was to utilize the free space information to improve track information for vehicles. We employ the probability model with two occupancy grid map (OGM) types, which are Bayesian theory and Dempster-Shafer theory based OGMs, to classify free space information states and to efficiently handle free space information. Final output from the proposed algorithm is the target vehicle’s compensated track. Experimental results verify superior performance compared with non-compensated algorithms.

     

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.     

基于V2I/V2N的感知融合系统技术及应用研究

随着智能驾驶技术的研究与发展,单车智能感知的局限性逐渐显现出来,基于V2X的感知技术解决了单车感知在遮挡等场景下的局限性。介绍一种基于V2I/V2N的感知融合系统技术方案,阐述其在智能驾驶系统中发挥的作用。重点讨论路侧感知与车载感知融合技术的算法架构及功能,研究利用V2I/V2N技术搭建智能网联汽车感知系统的可行性,研究基于V2I/V2N的感知融合系统在前车跟车行驶场景、车辆穿越交叉路口场景、高速路匝道车辆汇入场景三种典型场景下的应用。可以为车路协同感知技术应用研究提供参考。     

面向自动驾驶目标检测的深度多模态融合技术

研究者关注利用多个传感器来提升自动驾驶中目标检测模型的准确率,因此对目标检测中的数据融合方法进行研究具有重要的学术和应用价值。为此,本文总结了近年来自动驾驶中深度目标检测模型中的数据融合方法。首先介绍了自动驾驶中深度目标检测技术和数据融合技术的发展,以及已有的研究综述;接着从多模态目标检测、数据融合的层次、数据融合的计算方法3个方面展开阐述,全面展现了该领域的前沿进展;此外,本文提出了数据融合的合理性分析,从方法、鲁棒性、冗余性3个角度对数据融合方法进行了讨论;最后讨论了融合方法的一些公开问题,并从挑战、策略和前景等方面作了总结。     

无线传感器网络疑误数据节点自动诊断方法

为了提高无线传感器网络疑误数据检测能力,提出基于轮换调度的无线传感器网络疑误数据节点自动诊断方法。通过采用分块区域特征匹配的方法,得到无线传感器网络疑误数据传输的梯度模型,采用资源优化分配方案,进行数据传输信道的均衡调度,得到节点部署分布模型。通过传感信息跟踪采样方法,得到采样信息分布,建立无线传感器网络疑误数据信息特征分析,通过分组特征检测方法进行无线传感器网络疑误数据的信息融合和空间融合调度,提取无线传感器网络疑误数据的关联规则特征集,通过统计信息分析和融合调度的方法,进行无线传感器网络疑误数据的聚类挖掘,采用预算估计算法,得到疑误数据节点定位优化,结合自主学习算法,实现无线传感器网络疑误数据节点的优化定位和诊断检测。仿真结果表明,采用该方法进行无线传感器网络疑误数据节点检测的自适应性较好,特征辨识能力较强。     

基于惯性传感器融合控制算法的聋哑手语识别

聋哑人如何与外界进行有效沟通一直是一个备受关注的难点问题。文中提出了一种基于惯性传感器融合控制算法的手语识别方案,旨在实现高效准确的实时手语识别。该融合控制算法采用反馈控制思想,对两种传统的姿态信息计算方法进行融合,减少了环境对传感器的影响,可以准确获取被测对象在瞬时状态下的姿态信息。该算法通过对自采的聋哑手语数据进行数据融合、数据预处理和特征提取等处理,利用支持向量机、K-近邻法和前馈神经网络分类器自适应模型集成的分类方法进行分类。结果显示,所提传感器融合控制算法有效地得出了实时姿态,该手语识别方案对30种聋哑拼音手语的识别准确率达到96.5%。所提方案将为聋哑人手语识别打下坚实的基础,并为传感器融合控制的相关研究提供参考。