多传感器数据融合手册的关键技术探索

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"Handbook of Multisensor Data Fusion" 《多传感器数据融合手册》是一本深入探讨多传感器数据融合领域的权威著作。这本书包含了来自可靠来源的最新信息和研究成果，并且引用了经过许可的材料，对来源进行了清晰的标注。作者和出版商在确保提供可靠的数据和信息方面做出了合理努力，但不承担所有材料的准确性和使用后果的责任。本书的内容可能不被未经出版商事先书面许可的任何形式或任何手段复制或传播，无论是电子方式还是机械方式，包括影印、缩微胶片、录音或任何信息存储和检索系统。根据CRC Press LLC的规定，如果要进行内部或个人使用，或者特定客户个人或内部使用的复制，需要支付相应的费用。目前，每页影印需支付$1.50，费用应直接支付给美国版权清算中心。对于交易报告服务的用户，使用的ISBN代码是0-8493-2379-7/01/$0.00+$1.50，但此费用可能会在没有通知的情况下变动。多传感器数据融合是现代信息技术中的一个重要领域，它涉及如何将多个不同类型的传感器数据有效地整合在一起，以提高信息的精度、可靠性和决策制定能力。这个过程通常涉及到数据处理、模式识别、概率理论、统计分析、信号处理和机器学习等多个领域的知识。通过多传感器数据融合，可以克服单个传感器的局限性，例如感知范围、分辨率、精度等问题，从而在目标识别、环境监测、军事应用、自动驾驶、无人机系统等领域发挥重要作用。本书可能会涵盖以下关键知识点： 1. 多传感器系统设计：介绍如何构建和集成多个传感器系统，以实现协同工作和信息共享。 2. 数据融合层次：讨论不同层次的数据融合，如传感器级、特征级、决策级等，以及它们各自的优势和应用场景。 3. 传感器管理和校准：阐述如何管理和校准传感器以减小误差并确保数据一致性。 4. 概率与统计方法：讲解如何使用概率模型和统计方法来处理不确定性，如贝叶斯理论、卡尔曼滤波、粒子滤波等。 5. 信息融合算法：介绍各种信息融合算法，如加权平均法、Dempster-Shafer理论、模糊逻辑、神经网络等。 6. 实时性和性能评估：探讨如何实现数据融合的实时处理，并介绍性能评估指标和方法。 7. 应用案例分析：通过实际案例展示多传感器数据融合在不同领域的应用，如军事目标跟踪、环境监控、智能交通系统等。《多传感器数据融合手册》作为一本详尽的参考书，为读者提供了深入理解这一领域所需的知识和技术，对于研究人员、工程师和学生来说，是理解和应用多传感器数据融合技术的重要资源。

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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