多传感器数据融合手册：权威教材解析

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"《Handbook of Multisensor Data Fusion》是一本权威的多传感器数据融合教材，由CRC Press LLC在2001年出版。本书包含来自可靠来源的信息，并引用了经过许可的材料，同时列出了多种参考资料。尽管已尽最大努力提供准确的数据和信息，但作者和出版商不对所有材料的正确性或使用后果承担责任。本书的任何部分未经出版商事先书面许可，不得以任何形式复制或传播。对于内部或个人使用，或特定客户内部使用的复印件，可能需要支付给CRC Press LLC相应的版权费用。" 《Handbook of Multisensor Data Fusion》是研究多传感器融合技术的重要参考书，该技术是现代信息技术领域的一个关键组成部分，特别是在自动化、机器人、航空、军事和环境监测等广泛应用中。多传感器融合涉及到多个传感器数据的集成、处理和分析，以提高系统性能、准确性和可靠性。书中可能涵盖了以下几个核心知识点： 1. **传感器理论**：介绍不同类型传感器的工作原理，如光学、声学、热成像、雷达和激光雷达（LiDAR）等，以及它们在数据采集中的角色。 2. **数据融合框架**：探讨不同的数据融合层次，包括低级融合（传感器级）、中级融合（特征级）和高级融合（决策级），以及这些层次如何协同工作以优化整体信息理解。 3. **融合算法**：讨论各种数据融合算法，如卡尔曼滤波、粒子滤波、贝叶斯网络、模糊逻辑和神经网络，以及它们在处理不确定性、噪声和传感器间同步问题上的应用。 4. **实时处理**：介绍如何在有限计算资源和时间约束下实现高效的数据融合，包括实时数据处理策略和优化方法。 5. **系统设计与实施**：讲述多传感器系统的架构设计，包括硬件集成、软件接口、通信协议和系统评估。 6. **误差分析与校正**：分析传感器误差源，如偏置、漂移和噪声，以及如何通过误差模型进行校正。 7. **安全性与隐私**：探讨多传感器数据融合在确保数据安全和用户隐私方面的挑战及应对措施。 8. **案例研究**：提供实际应用案例，展示多传感器融合技术在自动驾驶、遥感、目标识别和环境监控等领域的应用效果。 9. **未来趋势**：对多传感器融合技术的未来发展方向和潜在研究课题进行展望，如物联网(IoT)中的融合、深度学习在融合中的作用等。通过深入学习《Handbook of Multisensor Data Fusion》，读者可以掌握多传感器数据融合的基本理论、方法和技术，从而在实际项目中实现更智能、更可靠的系统设计。

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

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

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

independent observations (e.g., the estimate of the target location or velocity is

improved by a factor proportional to

), assuming the data are combined in an optimal manner. This

same result could also be obtained by combining

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.

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

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多传感器数据融合手册：权威教材解析

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