没有合适的资源?快使用搜索试试~ 我知道了~
首页无线传感器数据融合权威指南
《多传感器数据融合手册》是一本由CRC Press LLC于2001年出版的专业书籍,专注于无线传感器网络以及多传感器数据融合领域的深入探讨。该书汇集了来自权威且备受尊重的信息来源,其中包含经过许可重印的部分内容,并对引用的资料进行了标注。作者和出版商已经尽力确保提供可靠的数据和信息,但并不能对所有材料的有效性或其使用后果承担责任。 本书强调了在信息技术快速发展的背景下,如何有效地整合多个传感器(如温度、湿度、光照、运动等不同类型传感器)收集的数据,以提高精确度、可靠性和决策支持。多传感器数据融合技术的应用广泛,包括环境监测、智能建筑、自动驾驶、工业自动化、医疗健康等领域。 书中详细讲解了数据融合的基本原理,包括卡尔曼滤波、粒子滤波、协方差矩阵估计等方法,以及它们在处理传感器噪声、冗余信息和动态环境中的应用策略。此外,还涉及到了无线传感器网络的设计与部署,包括通信协议的选择、能量管理、网络安全等问题。 值得注意的是,根据版权规定,未经事先书面许可,任何复制或传播整本书或部分内容的行为都是被禁止的,即使是内部或个人使用,也需向CRC Press LLC申请并支付每页1.50美元的复印费用。版权许可代码和适用的费用标准会在交易报告服务上提供,且可能随时间而调整。 《多传感器数据融合手册》为读者提供了深入理解多传感器数据融合技术的宝贵资源,对于希望在这个领域进行研究、开发或者应用的人来说,它是一个不可或缺的参考资料。通过学习这本书,读者不仅能掌握理论知识,还能学会如何在实际场景中优化数据融合算法,提升系统的性能和效率。
资源详情
资源推荐
![](https://csdnimg.cn/release/download_crawler_static/4082802/bg10.jpg)
©2001 CRC Press LLC
26.4 Evaluation Methodology
References
Part V Resources
Web Sites and News Groups Related to Data Fusion
Data Fusion Web Sites
News Groups
Other World Wide Web Information
Government Laboratories and Agencies
![](https://csdnimg.cn/release/download_crawler_static/4082802/bg11.jpg)
©2001 CRC Press LLC
I
Introduction
to Multisensor
Data Fusion
1 Multisensor Data Fusion
David L. Hall and James Llinas
Introduction • Multisensor Advantages • Military Applications • Nonmilitary
Applications • Three Processing Architectures • A Data Fusion Process Model •
Assessment of the State of the Art • Additional Information
2 Revisions to the JDL Data Fusion Model
Alan N. Steinberg and
Christopher L. Bowman
Introduction • What Is Data Fusion? What Isn’t? • Models and Architectures • Beyond
the Physical • Comparison with Other Models • Summary
3 Introduction to the Algorithmics of Data Association in Multiple-Target
Tracking
Jeffrey K. Uhlmann
Introduction •Ternary Trees • Priority
kd
-Trees • Conclusion • Acknowledgments
4 The Principles and Practice of Image and Spatial Data Fusion
Ed Waltz
Introduction • Motivations for Combining Image and Spatial Data • Defining Image and
Spatial Data Fusion • Three Classic Levels of Combination for Multisensor Automatic Target
Recognition Data Fusion • Image Data Fusion for Enhancement of Imagery Data • Spatial
Data Fusion Applications • Summary
5 Data Registration
Richard R. Brooks and Lynne Grewe
Introduction • Registration Problem • Review of Existing Research • Registration Using
Meta-Heuristics • Wavelet-Based Registration of Range Images • Registration
Assistance/Preprocessing • Conclusion • Acknowledgments
6 Data Fusion Automation: A Top-Down Perspective
Richard Antony
Introduction • Biologically Motivated Fusion Process Model • Fusion Process Model
Extensions • Observations • Acknowledgments
7 Contrasting Approaches to Combine Evidence
Joseph W. Carl
Introduction • Alternative Approaches to Combine Evidence • An Example Data Fusion
System • Contrasts and Conclusion • Appendix 7.A The Axiomatic Definition of
Probability
![](https://csdnimg.cn/release/download_crawler_static/4082802/bg12.jpg)
©2001 CRC Press LLC
1
Multisensor
Data Fusion
1.1 Introduction
1.2 Multisensor Advantages
1.3 Military Applications
1.4 Nonmilitary Applications
1.5 Three Processing Architectures
1.6 A Data Fusion Process Model
1.7 Assessment of the State of the Art
1.8 Additional Information
Reference
Integration or fusion of data from multiple sensors improves the accuracy of applications ranging from
target tracking and battlefield surveillance to nondefense applications such as industrial process moni-
toring and medical diagnosis.
1.1 Introduction
In recent years, significant attention has focused on multisensor data fusion for both military and
nonmilitary applications. Data fusion techniques combine data from multiple sensors and related infor-
mation to achieve more specific inferences than could be achieved by using a single, independent sensor.
The concept of multisensor data fusion is hardly new. As humans and animals have evolved, they have
developed the ability to use multiple senses to help them survive. For example, assessing the quality of
an edible substance may not be possible using only the sense of vision; the combination of sight, touch,
smell, and taste is far more effective. Similarly, when vision is limited by structures and vegetation, the
sense of hearing can provide advanced warning of impending dangers. Thus, multisensory data fusion
is naturally performed by animals and humans to assess more accurately the surrounding environment
and to identify threats, thereby improving their chances of survival.
While the concept of data fusion is not new, the emergence of new sensors, advanced processing
techniques, and improved processing hardware have made real-time fusion of data increasingly viable.
Just as the advent of symbolic processing computers (e.g., the SYMBOLICs computer and the Lambda
machine) in the early 1970s provided an impetus to artificial intelligence, recent advances in computing
and sensing have provided the capability to emulate, in hardware and software, the natural data fusion
capabilities of humans and animals. Currently, data fusion systems are used extensively for target tracking,
automated identification of targets, and limited automated reasoning applications. Data fusion technol-
ogy has rapidly advanced from a loose collection of related techniques to an emerging true engineering
David L. Hall
The Pennsylvania State University
James Llinas
State University of New York
![](https://csdnimg.cn/release/download_crawler_static/4082802/bg13.jpg)
©2001 CRC Press LLC
discipline with standardized terminology, collections of robust mathematical techniques, and established
system design principles.
Applications for multisensor data fusion are widespread. Military applications include automated
target recognition (e.g., for smart weapons), guidance for autonomous vehicles, remote sensing, battle-
field surveillance, and automated threat recognition systems, such as identification-friend-foe-neutral
(IFFN) systems. Nonmilitary applications include monitoring of manufacturing processes, condition-
based maintenance of complex machinery, robotics, and medical applications.
Te c hniques to combine or fuse data are drawn from a diverse set of more traditional disciplines,
including digital signal processing, statistical estimation, control theory, artificial intelligence, and classic
numerical methods. Historically, data fusion methods were developed primarily for military applications.
However, in recent years, these methods have been applied to civilian applications and a bidirectional
transfer of technology has begun.
1.2 Multisensor Advantages
Fused data from multiple sensors provides several advantages over data from a single sensor. First, if
several identical sensors are used (e.g., identical radars tracking a moving object), combining the obser-
vations will result in an improved estimate of the target position and velocity. A statistical advantage is
gained by adding the
N
independent observations (e.g., the estimate of the target location or velocity is
improved by a factor proportional to
N
), assuming the data are combined in an optimal manner. This
same result could also be obtained by combining
N
observations from an individual sensor.
A second advantage involves using the relative placement or motion of multiple sensors to improve
the observation process. For example, two sensors that measure angular directions to an object can be
coordinated to determine the position of an object by triangulation. This technique is used in surveying
and for commercial navigation. Similarly, the use of two sensors, one moving in a known way with
respect to another, can be used to measure instantaneously an object’s position and velocity with respect
to the observing sensors.
A third advantage gained by using multiple sensors is improved observability. Broadening the baseline
of physical observables can result in significant improvements. Figure 1.1 provides a simple example of
a moving object, such as an aircraft, that is observed by both a pulsed radar and a forward-looking
infrared (FLIR) imaging sensor. The radar can accurately determine the aircraft’s range but has a limited
ability to determine the angular direction of the aircraft. By contrast, the infrared imaging sensor can
accurately determine the aircraft’s angular direction but cannot measure range. If these two observations
are correctly associated (as shown in Figure 1.1), the combination of the two sensors provides a better
FIGURE 1.1
A moving object observed by both a pulsed radar and an infrared imaging sensor.
1
2
![](https://csdnimg.cn/release/download_crawler_static/4082802/bg14.jpg)
©2001 CRC Press LLC
determination of location than could be obtained by either of the two independent sensors. This results
in a reduced error region, as shown in the fused or combined location estimate. A similar effect may be
obtained in determining the identity of an object based on observations of an object’s attributes. For
example, there is evidence that bats identify their prey by a combination of factors, including size, texture
(based on acoustic signature), and kinematic behavior.
1.3 Military Applications
The Department of Defense (DoD) community focuses on problems involving the location, character-
ization, and identification of dynamic entities such as emitters, platforms, weapons, and military units.
These dynamic data are often termed an order-of-battle database or order-of-battle display (if superim-
posed on a map display). Beyond achieving an order-of-battle database, DoD users seek higher-level
inferences about the enemy situation (e.g., the relationships among entities and their relationships with
the environment and higher level enemy organizations). Examples of DoD-related applications include
ocean surveillance, air-to-air defense, battlefield intelligence, surveillance and target acquisition, and
strategic warning and defense. Each of these military applications involves a particular focus, a sensor
suite, a desired set of inferences, and a unique set of challenges, as shown in Table 1.1.
Ocean surveillance systems are designed to detect, track, and identify ocean-based targets and events.
Examples include antisubmarine warfare systems to support Navy tactical fleet operations and automated
systems to guide autonomous vehicles. Sensor suites can include radar, sonar, electronic intelligence
(ELINT), observation of communications traffic, infrared, and synthetic aperture radar (SAR) observa-
tions. The surveillance volume for ocean surveillance may encompass hundreds of nautical miles and
focus on air, surface, and subsurface targets. Multiple surveillance platforms can be involved and numer-
ous targets can be tracked. Challenges to ocean surveillance involve the large surveillance volume, the
combination of targets and sensors, and the complex signal propagation environment — especially for
underwater sonar sensing. An example of an ocean surveillance system is shown in Figure 1.2.
Air-to-air and surface-to-air defense systems have been developed by the military to detect, track, and
identify aircraft and anti-aircraft weapons and sensors. These defense systems use sensors such as radar,
passive electronic support measures (ESM), infrared identification-friend-foe (IFF) sensors, electro-optic
TABLE 1.1
Representative Data Fusion Applications for Defense Systems
Specific Applications
Inferences Sought by Data
Fusion Process
Primary Observable
Data
Surveillance
Vo lume
Sensor
Platforms
Ocean surveillance Detection, tracking,
identification of targets
and events
EM signals
Acoustic signals
Nuclear-related
Derived observations
Hundreds of
nautical miles
Air/surface/sub-
surface
Ships
Aircraft
Submarines
Ground-based
Ocean-based
Air-to-air and surface-
to-air defense
Detection, tracking,
identification of aircraft
EM radiation Hundreds of miles
(strategic)
Miles (tactical)
Ground-based
Aircraft
Battlefield intelligence,
surveillance, and
target acquisition
Detection and
identification of potential
ground targets
EM radiation Tens of hundreds
of miles about a
battlefield
Ground-based
Aircraft
Strategic warning and
defense
Detection of indications of
impending strategic
actions
Detection and tracking of
ballistic missiles and
warheads
EM radiation
Nuclear-related
Global Satellites
Aircraft
剩余536页未读,继续阅读
![-](https://img-home.csdnimg.cn/images/20210720083327.png)
![-](https://img-home.csdnimg.cn/images/20210720083327.png)
![-](https://img-home.csdnimg.cn/images/20210720083327.png)
![-](https://img-home.csdnimg.cn/images/20210720083327.png)
![-](https://img-home.csdnimg.cn/images/20210720083327.png)
![-](https://csdnimg.cn/download_wenku/file_type_lunwen.png)
![-](https://csdnimg.cn/download_wenku/file_type_lunwen.png)
![-](https://csdnimg.cn/download_wenku/file_type_lunwen.png)
![-](https://csdnimg.cn/download_wenku/file_type_lunwen.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://profile-avatar.csdnimg.cn/a9c6124f2933403591cf84645beeb17f_davidyuxuan.jpg!1)
DavidYuxuan
- 粉丝: 12
- 资源: 36
上传资源 快速赚钱
我的内容管理 展开
我的资源 快来上传第一个资源
我的收益
登录查看自己的收益我的积分 登录查看自己的积分
我的C币 登录后查看C币余额
我的收藏
我的下载
下载帮助
![](https://csdnimg.cn/release/wenkucmsfe/public/img/voice.245cc511.png)
最新资源
- 构建Cadence PSpice仿真模型库教程
- VMware 10.0安装指南:步骤详解与网络、文件共享解决方案
- 中国互联网20周年必读:影响行业的100本经典书籍
- SQL Server 2000 Analysis Services的经典MDX查询示例
- VC6.0 MFC操作Excel教程:亲测Win7下的应用与保存技巧
- 使用Python NetworkX处理网络图
- 科技驱动:计算机控制技术的革新与应用
- MF-1型机器人硬件与robobasic编程详解
- ADC性能指标解析:超越位数、SNR和谐波
- 通用示波器改造为逻辑分析仪:0-1字符显示与电路设计
- C++实现TCP控制台客户端
- SOA架构下ESB在卷烟厂的信息整合与决策支持
- 三维人脸识别:技术进展与应用解析
- 单张人脸图像的眼镜边框自动去除方法
- C语言绘制图形:余弦曲线与正弦函数示例
- Matlab 文件操作入门:fopen、fclose、fprintf、fscanf 等函数使用详解
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
![](https://img-home.csdnimg.cn/images/20220527035711.png)
![](https://img-home.csdnimg.cn/images/20220527035711.png)
![](https://img-home.csdnimg.cn/images/20220527035111.png)
安全验证
文档复制为VIP权益,开通VIP直接复制
![](https://csdnimg.cn/release/wenkucmsfe/public/img/green-success.6a4acb44.png)