数据融合算法精华：信息评估与决策的关键工具

3星 · 超过75%的资源需积分: 50 112 浏览量更新于2024-07-20 11 收藏 5.01MB PDF 举报

身份认证购VIP最低享 7 折!

领优惠券(最高得80元）

"数据融合算法精华，一本专注于数据和传感器信息融合的专业书籍，由Lawrence A. Klein撰写，适合技术人员快速理解和应用数据融合技术。" 在信息技术领域，数据融合与信息融合是关键的议题，尤其在传感器技术的应用中，它们扮演着至关重要的角色。Lawrence A. Klein的《Sensor and Data Fusion: A Tool for Information Assessment and Decision Making》深入探讨了如何通过这些技术进行有效的情报评估和决策制定。数据融合，也称为多源数据融合，是指将来自不同来源或不同类型的传感器数据整合在一起，以提供更全面、准确的信息视图。这种技术尤其在环境监测、军事应用、自动驾驶、物联网(IoT)等领域有着广泛的应用。通过数据融合，我们可以弥补单个传感器的局限性，比如探测范围、精度或者抗干扰能力，从而获得更高层次的理解和洞察。信息融合则更进一步，它不仅仅是简单的数据聚合，而是对多个信息源的数据进行分析和综合，提取出更有价值的知识和理解。这涉及到信号处理的数字技术，包括滤波、特征提取、模式识别等方法，以确保融合后的信息具有更高的可信度和准确性。本书涵盖了信号处理的数字技术，这是数据融合的基础。这些技术可能包括但不限于数字滤波器设计、卡尔曼滤波、粒子滤波、贝叶斯估计等，它们用于处理和解析传感器数据，去除噪声，提升数据质量。同时，书中可能会讨论多传感器数据融合策略，如层次融合、集中式融合和分布式融合，以及如何根据具体应用场景选择合适的融合架构。此外，书中可能还涉及信息评估和决策制定的理论与实践，如决策理论、模糊逻辑、人工神经网络等，这些都是在融合信息后进行有效决策的关键。作者强调，尽管书中内容反映的是作者的工作和思想，但已尽力确保出版的信息可靠且实用，以帮助读者在实际工作中应用这些先进的数据融合技术。对于希望深入理解和应用数据融合技术的IT专业人士，这本书提供了一个全面的指南，无论是在理论层面还是实践层面，都能从中受益。通过学习本书，读者将能够更好地利用传感器数据，进行更精准的信息评估，并做出更明智的决策。

资源详情

资源推荐

CONTENTS

References ............................................................................................ 213

Chapter 8 Voting Logic Fusion .............................................................. 215

8.1 Sensor target reports ................................................................ 217

8.2 Sensor detection space ............................................................ 218

8.2.1 Venn diagram representation of detection space ....... 218

8.2.2 Confidence levels ..................................................... 218

8.2.3 Detection modes ....................................................... 219

8.3 System detection probability ................................................... 221

8.3.1 Derivation of system detection and false alarm

probability for nonnested confidence levels ............ 221

8.3.2 Relation of confidence levels to detection and false

alarm probabilities .................................................... 223

8.3.3 Evaluation of conditional probability ....................... 224

8.3.4 Establishing false alarm probability ......................... 225

8.3.5 Calculating system detection probability .................. 226

8.3.6 Summary of detection probability computation

model ......................................................................... 226

8.4 Application example without singleton sensor detection

modes ...................................................................................... 227

8.4.1 Satisfying the false alarm probability requirement .... 228

8.4.2 Satisfying the detection probability requirement ...... 228

8.4.3 Observations .............................................................. 230

8.5 Hardware implementation of voting logic sensor fusion ........ 231

8.6 Application example with singleton sensor detection modes .. 231

8.7 Comparison of voting logic fusion with Dempster-Shafer

evidential theory ....................................................................... 233

8.8 Summary .................................................................................. 234

References .......................................................................................... 235

Chapter 9 Fuzzy Logic and Fuzzy Neural Networks .......................... 237

9.1 Conditions under which fuzzy logic provides an appropriate

solution ..................................................................................... 237

9.2 Illustration of fuzzy logic in an automobile antilock

braking system .......................................................................... 238

9.3 Basic elements of a fuzzy system ............................................. 239

9.4 Fuzzy logic processing ............................................................. 239

9.5 Fuzzy centroid calculation ....................................................... 241

9.6 Balancing an inverted pendulum with fuzzy logic control ....... 243

9.6.1 Conventional mathematical solution ........................ 243

9.6.2 Fuzzy logic solution ................................................... 245

9.7 Fuzzy logic applied to multitarget tracking .............................. 248

9.7.1 Conventional Kalman filter approach ........................ 249

9.7.2 Fuzzy Kalman filter approach ................................... 251

9.8 Fuzzy neural networks ............................................................ 256

CONTENTS

9.9 Fusion of fuzzy-valued information from multiple

sources ..................................................................................... 258

9.10 Summary .................................................................................. 259

References .......................................................................................... 261

Chapter 10 Passive Data Association Techniques for Unambiguous

Location of Targets ................................................................ 263

10.1 Data fusion options ................................................................... 263

10.2 Received-signal fusion ............................................................. 265

10.2.1 Coherent processing technique .................................. 267

10.2.2 System design issues ................................................. 269

10.3 Angle data fusion ..................................................................... 271

10.3.1 Solution space for emitter locations ......................... 272

10.3.2 Zero-one integer programming algorithm

development............................................................... 275

10.3.3 Relaxation algorithm development ........................... 280

10.4 Decentralized fusion architecture ............................................ 282

10.4.1 Local optimization of direction angle track

association .................................................................. 283

10.4.2 Global optimization of direction angle track

association ................................................................. 285

10.5 Passive computation of range using tracks from a single sensor

site ............................................................................................ 287

10.6 Summary ................................................................................. 288

References .......................................................................................... 290

Chapter 11 Retrospective Comments ...................................................... 293

Appendix A Planck Radiation Law and Radiative Transfer ................. 299

A.1 Planck radiation law ............................................................... 299

A.2 Radiative transfer theory ......................................................... 301

References ........................................................................................ 305

Appendix B Bayesian Inference Applied to Freeway Incident

Detection .................................................................................. 307

B.1 Problem statement and development ........................................ 307

B.2 Numerical Examples ................................................................ 311

Appendix C Plausible and Paradoxical Reasoning ................................... 313

C.1 Example of unexpected results arising from conflicting

information sources .................................................................. 313

C.2 Plausible and paradoxical reasoning ........................................ 314

C.3 Resolution of medical diagnosis dilemma ................................ 316

Chapter 1

Introduction

Weather forecasting, battlefield assessment, target classification and tracking,

transportation management––these are but a few of the many civilian and defense

applications enhanced through the use of sensor and data fusion. The design of

effective sensor and data fusion architectures involves optimization of size, cost,

and performance of the sensors and associated data processing, which in turn

requires a broad spectrum of knowledge. Accordingly, sensor and data fusion

practitioners generally have an appreciation of (1) target and background

signature-generation phenomena, (2) sensor design, (3) signal processing

algorithms, (4) pertinent characteristics of the environment in which the sensors

operate, (5) available communications, and (6) end use of the fusion products.

This book discusses the above topics, with major emphasis on signature-

generation phenomena to which electromagnetic sensors respond, atmospheric

effects, sensor fusion architectures, and data fusion algorithms for target

detection, classification, identification, and tracking. The types of signatures and

data collected by a sensor are affected by the following:

x The type of energy (e.g., electromagnetic, acoustic, ultrasonic,

seismic) received by the sensor;

x active or passive sensor operation as influenced by center frequency,

polarization, spectral band, and incidence angle;

x spatial resolution of the sensor versus target size;

x target and sensor motion;

x weather, clutter, and countermeasure effects.

Although this book focuses on phenomena that affect electromagnetic sensors,

other sensors such as acoustic, ultrasonic, and seismic can also be a part of a

sensor fusion architecture. This group of sensors has proven valuable in civilian

applications, which include detection of vehicles on roadways, aircraft on

runways, and geological exploration. Military applications of these sensors

include the detection and classification of objects above and below ground. The

information that nonelectromagnetic sensors provide can certainly be part of a

data and sensor fusion architecture.

SENSOR AND DATA FUSION:ATOOL FOR INFORMATION ASSESSMENT AND DECISION MAKING

Once the signature-generation processes or observables are known, it is possible

to design a multiple sensor system that captures their unique attributes. Sensors

that respond to signatures generated by different physical phenomena can

subsequently be selected and their outputs combined to provide varying degrees

of immunity to weather, clutter, and diverse countermeasures. Oftentimes, the

data fusion process produces knowledge that is not otherwise obtainable from a

narrow wavelength-band sensor or is more accurate than information gathered

from single sensor systems. An example of the former is the identification of

vegetation on Earth through fusion of hyperspectral visible and infrared data

from space-based sensors such as the Airborne Visible/Infrared Imaging

Spectrometer (AVIRIS). The AVIRIS contains 224 detectors, each with a

spectral bandwidth of approximately 10 nm that cover the 380- to 2500-nm band.

Data fusion also improves the ability of missiles to track and defeat threats. In

this case, accuracy is enhanced by handing off the guidance required for final

missile impact from a lower resolution sensor, optimized for search, to a higher

resolution sensor optimized to find a particular impact area on a target.

The discussion of data fusion that appears in this book is based on the definition

derived from recommendations of the U.S. Department of Defense Joint

Directors of Laboratories Data Fusion Subpanel, namely,

Data fusion is a multilevel, multifaceted process dealing with the

automatic detection, association, correlation, estimation, and

combination of data and information from single and multiple sources to

achieve refined position and identity estimates, and complete and timely

assessments of situations and threats and their significance.

Data fusion consists of a collection of subdisciplines, some of which are more

mature than others. The more mature techniques, such as classical, Bayesian, and

Dempster-Shafer inference; fuzzy logic; pattern recognition using signal

processing algorithms and artificial neural networks; and multisensor, multitarget

tracking, draw on a theoretical apparatus that supports their application. The less

mature techniques are dominated by heuristic and ad hoc methods.

The terms data fusion and sensor fusion are often used interchangeably. Strictly

speaking, data fusion is defined as above. Sensor fusion, then, describes the use

of more than one sensor in a multisensor system to enable more accurate or

additional data to be gathered about events or objects present in the observation

space of the sensors. More than one sensor may be needed to fully monitor the

observation space at all times for a number of reasons: (1) some objects may be

detected by one sensor but not another because of the manner in which signatures

are generated, i.e., each sensor may respond to a different signature-generation

phenomenology; (2) the signature of an object may be masked or

countermeasured with respect to one sensor but not another; (3) one sensor may

be blocked from viewing objects because of the geometric relation of the sensor

剩余345页未读，继续阅读

普通网友

粉丝: 1
资源:
7

数据融合算法精华：信息评估与决策的关键工具

惯性导航matlab开源程序（GPS&IMU;数据融合）

PCA融合算法C++代码

多源数据关联与融合算法研究_王海颖.caj

决策树算法的集成学习精华：揭秘提升预测准确度的秘密

容错数据融合算法综述

starfm数据融合算法

多源异构数据融合算法

数据融合算法python

多源异构数据融合方法mcs-rf

基于多源数据融合的工业时序数据异常检测算法将

基于多源数据融合的工业时序数据异常检测算法

小波变换数据融合方法

多源融合算法matlab实现

多个传感器数据融合matlab

因此，可以重点关注基于多源数据融合的工业时序数据异常检测算法。

多传感器数据融合与应用.pdf下载

matlab点云数据融合

卡尔曼滤波数据融合 envi

无线传感器网络的数据融合技术有哪几种？画出他们的数据处理的结构过程

最新资源