Multisensor Data Fusion for High Quality Data Analysis and Processing in Measurement and Instrumentation

Multisensor data fusion （MDF） is an emerging technology to fuse data from multiple sensors in order to make a more accurate estimation of the environment through measurement and detection. Applications of MDF cross a wide spectrum in military and civilian areas. With the rapid evolution of computers and the proliferation of micro-mechanical/electrical systems sensors, the utilization of MDF is being popularized in research and applications. This paper focuses on application of MDF for high quality data analysis and processing in measurement and instrumentation. A practical, general data fusion scheme was established on the basis of feature extraction and merge of data from multiple sensors. This scheme integrates artificial neural networks for high performance pattern recognition. A number of successful applications in areas of NDI （Non-Destructive Inspection） corrosion detection, food quality and safety characterization, and precision agriculture are described and discussed in order to motivate new applications in these or other areas. This paper gives an overall picture of using the MDF method to increase the accuracy of data analysis and processing in measurement and instrumentation in different areas of applications.

Multiscale Multisensor Data Fusion and Application in High Precision Marking and Cutting Robot System

The multisensor online measure system for high precision marking and cutting robot system is designed and the data fusion method is introduced, which combines augment state multiscale process with extend Kalman filter. The technology measuring the three-dimensional deforming information of profiled bars is applied. The experimental result shows that applying the multisensor data fusion technology can enhance the measure precision and the reliability of measure system.

Research on Kalman-filter based multisensor data fusion

Multisensor data fusion has played a significant role in diverse areas ranging from local robot guidance to global military theatre defense etc. Various multisensor data fusion methods have been extensively investigated by researchers, of which Klaman filtering is one of the most important. Kalman filtering is the best-known recursive least mean-square algorithm to optimally estimate the unknown states of a dynamic system, which has found widespread application in many areas. The scope of the work is restricted to investigate the various data fusion and track fusion techniques based on the Kalman Filter methods, then a new method of state fusion is proposed. Finally the simulation results demonstrate the effectiveness of the introduced method.

Distributed multisensor data fusion based on Kalman filtering and the parallel implementation

The purpose of data fusion is to produce an improved model or estimate of a system from a set of independent data sources. Various multisensor data fusion approaches exist, in which Kalman filtering is important. In this paper, a fusion algorithm based on multisensor systems is discussed and a distributed multisensor data fusion algorithm based on Kalman filtering presented. The algorithm has been implemented on cluster-based high performance computers. Experimental results show that the method produces precise estimation in considerably reduced execution time.

THEORETICAL ANALYSIS OF IMPROVEMENT OF TRACK LOSS IN CLUTTER WITH MULTISENSOR DATA FUSION

The paper analyses the improvement of track loss in clutter with multisensor data fusion.By a determination of the transition probability density function for the fusion prediction error, one can study the mechanism of track loss analytically. With nearest-neighbor association algorithm. The paper we studies the fused tracking performance parameters, such as mean time to lose fused track and the cumulative probability of lost fused track versus the normalized clutter density, for track continuation and track initiation, respectively. A comparison of the results obtained with the case of a single sensor is presented. These results show that the fused tracks of multisensor reduce the possibility of track loss and improve the tracking performance. The analysis is of great importance for further understanding the action of data fusion.

Feasibility analysis of angles-only navigation algorithm with multisensor data fusion for spacecraft noncooperative rendezvous

Relative navigation is crucial for spacecraft noncooperative rendezvous,and angles-only navigation using visible and infrared cameras provides a feasible solution.Herein,an angles-only navigation algorithm with multisensor data fusion is proposed to derive the relative motion states between two noncooperative spacecraft.First,the design model of the proposed algorithm is introduced,including the derivation of the state propagation and measurement equations.Subsequently,models for the sensor and actuator are introduced,and the effects of various factors on the sensors and actuators are considered.The square-root unscented Kalman filter is used to design the angles-only navigation filtering scherne.Additionally,the Clohessy-Wiltshire terminal guidance algorithm is introducedto obtain the theoretical relative motion trajectories during the rendezvous operations of two noncooperative spacecraft.Finally,the effectiveness of the proposed angles-only navigation algorithm is verified using a semi-physical simulation platform.The results prove that an optical navigation camera combined with average accelerometers and occasional orbital maneuvers is feasible for spacecraft noncooperative rendezvous using angles-only navigation.

Data Fusion Method for Manufacturing Measurement

A data fusion method of online multisensors is prop os ed in this paper based on artificial neuron. First, the dynamic data fusion mode l on artificial neuron is built. Then the calibration of data fusion is discusse d with self-adaptive weighing technique. Finally performance of the method is d emonstrated by an online vibration measurement case. The results show that the f used data are more stable, sensitive, accurate, reliable than that of single sen sor data.

优化的ID3算法在多传感器安防系统中的应用

针对实物保护系统(Physical Protection System,PPSY)中单一传感器报警准确率较低的问题,提出了一种基于改进ID3的CAC-ID3(Confidence And Correlation-ID3)算法在多传感器实物保护系统中数据融合的新方法。与传统的单一传感器数据信息处理相比,多传感器数据融合能够更加准确、全面的得到被测对象的数据信息,有效地利用多传感器资源。CAC-ID3算法首先在ID3的基础上引入属性置信度重新计算期望熵,解决属性和价值不对等的问题,克服多传感器数据分类时多值偏向的缺点,其值由经验和相关领域知识决定。然后通过引入属性间的相关度来调整信息增益值,提高分类精度。实验结果表明:基于CAC-ID3的决策树算法的多传感器PPSY能有效提高报警准确率和可靠性,防止敌对分子入侵,提高传感器对PPSY的检测的效能,且该算法的分类精度高于ID3算法。

Research in Control Systems, Sensor Fusion, and Haptic Interfaces

Research in control systems, sensor fusion and haptic interfaces is reviewed.

MULTISENSOR TRACKING SYSTEM WITH ATTITUDE MEASUREMENTS

A new 3 D fusion tracking system for an anti air missile homing system based on radar and imaging sensor is developed. The attitude measurements from the imaging sensor are used to improve the tracking performance. Computer simulation results show that the tracking system greatly reduces the tracking errors compared with trackers without attitude measurements, and achieves small miss distances even when the target has a big maneuver.

DISTRIBUTED CFAR SIGNAL DETECTION BASED ON AREA FUSION

The multisensor detection area partitioning is considered. An approach is presented to the fusion in each detection area where the sensor uses different thresholds and then at system level. The expressions of the detection probability and false alarm probability are given. An application of the method is illustrated to distributed CFAR detection systems. The result shows that the system detection probability may be improved by setting different thresholds for a detector.

Relationship Between Track Fusion Solutions with and without Feedback Information

In distributed multisensor data fusion systems, there are two types of track fusion approaches. One is sensor track fusion with feedback information, the other is without feedback information. This paper proves that the solutions of sensor track fusion with and without feedback information are both optimal and equal.

多传感器数据融合技术在畜禽养殖领域的应用研究

在多传感器数据融合技术的多功能性和不同应用领域的推动下,人们对相关技术的研究越来越感兴趣。鉴于此,对多传感器数据融合技术在畜禽养殖领域的研究现状进行综述,探讨了其技术短板并对该技术在畜禽养殖领域的前景进行了展望。

基于多传感器数据融合的播深检测系统研究

目前,播种深度的实时检测存在检测精度不高、可靠性差及数据处理不足的问题。为此,在分析了播种单体四连杆运动机理后,提出了一种基于多传感器数据融合的播种深度检测方法。该方法采用角度传感器、激光传感器、超声波传感器作为播种深度检测,利用播种单体四连杆仿形上下平动特性,测量播种深度变化,并利用卡尔曼滤波与加权平均相结合的方式进行多传感器数据融合。试验与软件仿真验证表明:系统稳定可靠,且检测精度在±0.04cm以内,说明多传感器数据融合后的估计值相较单独传感器检测数值更能准确反应播种深度变化,为后期进行播种深度控制提供数据支撑。

基于改进多传感器数据融合算法的温室环境检测研究

采用传统方法进行温室环境参数检测,通常存在环境复杂、检测可靠性低、精度差等问题。为提高温室大棚多参数检测数据的准确性,提出一种改进的多传感器数据融合算法。首先利用箱线图算法剔除偏离大的传感器数据,得到最优数据集;其次使用支持度和置信距离理论构建新的支持矩阵,将剔除的异常数据用支持度最高值代替,提高参与融合的数据可靠性;然后利用改进的自适应加权算法对数据进行融合;最后经测试,对传感器数据融合算法和算术加权平均融合算法处理结果进行分析比较。实验结果表明,所提算法能够提高温室环境参数检测的精度,融合值的相对误差更低,稳健性较好。

多平台声纳水下目标异步航迹序贯融合方法

多平台协同探测是目前舰船编队水下目标探测的一种主流形式。然而,由于平台间存在探测、通信等方面造成的信息差异,导致数据融合过程中存在数据异步问题,难以融合形成目标航迹。为此,论文以序贯融合思想为基础,构建了异步数据融合方法框架,并改进了数据关联、融合算法,增加了航迹滤波算法,提出了一种新颖的多平台声纳水声目标异步数据的融合方法。仿真和两批实验数据处理结果表明,该方法在实际应用中具有解决上述问题能力。

分体式飞行汽车全自主对接导引系统设计与验证

研究针对分体式飞行汽车全自主对接导引系统的流程架构、软硬件系统、核心算法以及验证.根据导引方式的过渡,采用远程、中程、近程多段融合导引.针对YOLOv4-tiny实际使用中的误检、漏检情况,使用点密度聚类和核相关滤波算法提供光顺的融合信息.提出修正因子方法以实现近程导引阶段AprilTag测量数据的融合修正,通过姿态补偿算法解决相机与无人机固连的相机姿态问题.引入暗光图像增强算法,将引入算法与视觉导引算法结合,以满足低照度环境下的对接导引需求.搭建仿真平台和工程应用平台,逐步对发展的流程、系统架构以及算法进行验证.试验结果表明,工程应用飞行平台可以导引安全、平稳且精准的降落任务,在圆锥形对接机构中的容许误差为6 cm、角度误差为5°.该结果证明提出的全自主对接导引技术精度良好且具有可靠性.

一种天基平台多传感器协同及数据融合方法

提出了一种星载电子侦察传感器和微波成像传感器的协同方法,能够在单一的天基平台上,通过星载电子侦察在大范围内搜索目标,并引导星载微波成像设备进行补充侦察。通过2种侦察结果的柔性重组,支撑精确定位与精细化判读。分析了平台定位误差、姿态误差和信号频段等因素对协同侦察任务的影响,分析结果证明了本方法的实用性。

智能车辆多传感器融合策略

传统车辆自主换道研究多以理想道路曲率及车辆间简化的相对运动为前提,通过匹配采样周期将车路数据直接传入控制器,难以验证系统在真实环境下的可靠性。文章设计了一种多传感器布置方案及融合策略,并在Matlab/Simulink平台构建高速公路仿真场景,验证多传感器融合策略的有效性。其仿真结果表明,该多传感器融合策略能够在高速场景下实现对目标车辆的持续跟踪,模拟智能车辆环境感知、数据融合过程,进而为智能车辆自主换道系统的开发提供技术参考。

多传感器数据融合中的配准技术

配准是多传感器数据融合中的一个重要环节。文中介绍了图像配准的一般方法，配准技术在数据融合中的应用，并着重介绍了基于点特征的红外／可见光图像的自动配准算法。