MATLAB在多传感器数据融合中的应用

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"Multi-Sensor Data Fusion with MATLAB" 是一本由 Jitendra R. Raol 编著的专业书籍，由 Taylor & Francis Group 出版。该书深入探讨了多传感器信息融合这一主题，并利用 MATLAB 这一强大的计算工具进行示例和模拟。在现代科技领域，多传感器数据融合技术是关键的组成部分，它涉及从多个不同来源的传感器中集成和分析数据，以提高系统性能、准确性和可靠性。这本书旨在为读者提供多传感器融合的理论基础以及实际应用的方法。通过使用 MATLAB，作者为读者提供了实现这些概念的实践经验，使得复杂的数学模型和算法变得易于理解和实现。 MATLAB 是一个广泛应用于工程、科学和经济领域的数值计算软件，而 Simulink 则是其用于动态系统建模和仿真的一个附加工具。在这本书中，Raol 教授可能详细介绍了如何使用 MATLAB 和 Simulink 来设计、测试和优化多传感器融合算法，包括数据预处理、融合策略（如层次融合、分布式融合和集中式融合）、决策制定以及误差处理等。本书可能涵盖了以下几个核心知识点： 1. **多传感器系统理论**：解释了多传感器系统的架构，包括传感器选择、同步、标定以及数据关联等基本概念。 2. **数据融合模型**：介绍经典的融合模型，如卡尔曼滤波器、贝叶斯网络、模糊逻辑和神经网络等，并探讨它们在多传感器环境中的应用。 3. **MATLAB 实现**：通过实例展示如何使用 MATLAB 编程实现各种数据融合算法，帮助读者将理论转化为实践。 4. **仿真与评估**：讲解如何使用 Simulink 进行系统仿真，评估融合算法的性能和稳定性，并提供了一套评估融合效果的标准和方法。 5. **应用案例**：可能包含来自不同领域的应用案例，如自动驾驶、无人机导航、目标检测与跟踪、环境监测等领域，展示了多传感器数据融合的实际价值。 6. **挑战与解决方案**：讨论多传感器融合中的挑战，如数据不一致性、传感器故障和通信延迟，并提出相应的解决策略。 7. **误差分析与鲁棒性**：分析传感器误差对融合结果的影响，以及如何构建鲁棒的融合系统以抵御不确定性和噪声。这本书对于希望深入理解多传感器数据融合技术，以及想通过 MATLAB 实践这一技术的工程师、研究人员和学生来说，是一份宝贵的资源。它不仅提供了理论知识，还强调了实践应用，有助于提升读者在实际项目中的能力。

xviii Contents

13.3 Proximity- and Condition-Monitoring Systems ............................... 488

Epilogue ........................................................................................................... 491

Exercises .......................................................................................................... 492

References ........................................................................................................ 492

Appendix: Numerical, Statistical, and Estimation Methods ............... 495

A.1 Some De nitions and Concepts .......................................................... 495

A.1.1 Autocorrelation Function......................................................... 495

A.1.2 Bias in Estimate ......................................................................... 496

A.1.3 Bayes’ Theorem ......................................................................... 496

A.1.4 Chi-Square Test ......................................................................... 496

A.1.5 Consistency of Estimates Obtained from Data .................... 496

A.1.6 Correlation Coef cients and Covariance .............................. 497

A.1.7 Mathematical Expectations ..................................................... 497

A.1.8 Ef cient Estimators ................................................................... 498

A.1.9 Mean-Squared Error (MSE) ..................................................... 498

A.1.10 Mode and Median..................................................................... 498

A.1.11 Monte Carlo Data Simulation .................................................. 498

A.1.12 Probability .................................................................................. 499

A.2 Decision Fusion Approaches ............................................................... 499

A.3 Classi er Fusion ....................................................................................500

A.3.1 Classi er Ensemble Combining Methods ............................. 501

A.3.1.1 Methods for Creating Ensemble Members ............ 501

A.3.1.2 Methods for Combining Classi ers in Ensembles ... 501

A.4 Wavelet Transforms .............................................................................. 502

A.5 Type-2 Fuzzy Logic ............................................................................... 504

A.6 Neural Networks .................................................................................. 505

A.6.1 Feed-Forward Neural Networks ............................................ 506

A.6.2 Recurrent Neural Networks ....................................................508

A.7 Genetic Algorithm ................................................................................508

A.7.1 Chromosomes, Populations, and Fitness ..............................509

A.7.2 Reproduction, Crossover, Mutation, and Generation .......... 509

A.8 System Identi cation and Parameter Estimation ............................. 509

A.8.1 Least-Squares Method .............................................................. 510

A.8.2 Maximum Likelihood and Output Error Methods .............511

A.9 Reliability in Information Fusion ........................................................516

A.9.1 Bayesian Method ....................................................................... 518

A.9.1.1 Weighted Average Methods ...................................... 518

A.9.2 Evidential Methods .................................................................. 518

A.9.3 Fuzzy Logic–Based Possibility Approach ............................. 519

A.10 Principal Component Analysis ........................................................... 519

A.11 Reliability ............................................................................................... 520

References ........................................................................................................ 520

xix

Preface

The human brain routinely and almost spontaneously carries out a lot of

information processing and fusion. This is possible due to the biological

neural networks in our brains, which are actually parallel processing sys-

tems of adaptive switching (chemical circuits) units. The main objectives

are to collect measurements and simple observations from various similar

or dissimilar sources and sensors, extract the required information, draw

logical inferences, and then combine or fuse these with a view toward

obtaining an enhanced status and identity of the perceived or observed

object, scene, or phenomenon. These acts of information processing and

decision making are very crucial for the survival and growth of human

beings, as well as many other living creatures, and can be termed multi-

source multisensor information fusion (MUSSIF), more popularly known as

sensor data fusion (DF).

MUSSIF is very rapidly emerging as an independent discipline to be

reckoned with and  nds ever-increasing applications in many biomedical,

automation industry, aerospace, robotics, and environmental engineering

processes and systems, in addition to typical defense applications. MUSSIF

offers one or more of the following bene ts: more spatial coverage of the

object under observation, redundancy of measurements, robustness of

the system’s performance and higher accuracy (basically reduced uncer-

tainty of prediction) of inferences, and an overall assured performance of

the sensor-integrated systems. The complete process of MUSSIF involves

the study of several related disciplines: (1) signal and image processing;

(2) numerical computational algorithms; (3) statistical and probabilistic

approaches and methods; (4) sensor modeling, management, control, and

optimization; (5) neural networks, fuzzy logic systems, and genetic algo-

rithms; (6) system identi cation and state or parameter estimation; and

(7) database management. Many principles and techniques from these

 elds strengthen the de nition of tasks, analysis, and performance evalu-

ations of multisensor DF (MSDF) systems. Several of these aspects are

brie y discussed in this book.

In this book, theories, concepts, and applications of MSDF are treated

in three parts: (1) kinematic-level fusion (including theory of DF);

(2) fuzzy logic and decision fusion; and (3) pixel- and feature-level image

fusion. The development elucidates aspects and concepts of DF strategies,

algorithms, and performance evaluations, mainly for aerospace applica-

tions. However, the concepts and methods discussed are equally appli-

cable to other systems. Where possible, this is illustrated with examples

via numerical simulations coded in MATLAB

. (MATLAB

is the trade

xxi

Acknowledgments

Researchers all over the world have been making important contributions

to this specialized  eld for the last three to four decades. This  eld is rap-

idly emerging as an enabling technology to be reckoned with, especially

in aerospace science and technology applications, robotics, and some

industrial spin-offs.

More than a decade ago, DF research and development was also initiated

in defense laboratories in India, with a major focus on target tracking and

building expert systems to aid range safety of cers at  ight testing agen-

cies. The authors are very grateful to Air Commodores P. Banerjee and

R. Appavu Raj (of Integrated Test Range [ITR], Defence Research and

Development Organisation [DRDO]) for conceiving of the idea of the

RTFLEX expert system, which gave an incentive to a chain of develop-

mental projects from other organizations in the country in the area of

MSDF. Some theoretical and software development work was also ini-

tiated in certain educational institutions. The authors are very grateful

to Dr. T. S. Prahlad and Dr. S. Srinathkumar of the National Aerospace

Laboratories (NAL) for supporting DF activities for more than a decade.

The authors are also very grateful to Professor T. K. Ghoshal of Jadavapur

University in Kolkata for his support of DF research in various forms and

for various applications. The authors are also grateful to Dr. V. V. Murthy

and Dr. R. N. Bhattacharjee of the Defence Research and Development

Laboratory (DRDL) in Hyderabad, and to Professor Ananthasayanam at

The Indian Institute of Science (IISc) in Bangalore for their technical and

moral support of DF activities at NAL. We are also grateful to the previ-

ous and present directors of NAL for supporting DF research and project

activities.

Dr. Raol is very grateful to Dr. S. Balakrishna of NAL; Professor

N. K. Sinha (professor emeritus at McMaster University in Canada) Profes-

sor R. C. Desai (M.S. University of Baroda), Dr. Gangan Prathap (VC, CUSAT,

Kerala), Professor M. R. Kaimal (University of Kerala), Dr. M. R. Nayak

(Advisor, M and A, NAL), Professor P. R. Viswanath (NAL and IISc,

Bangalore), Dr. U. N. Sinha, Dr. R. M. V. G. K. Rao, and Dr. T. G. Ramesh

for their encouragement and moral support for several years. The author is

very grateful to Professor Florin Ionescu, the Director of Mechatronics at

the University of Applied Sciences in Konstanz, Germany for supporting

him during his DFG fellowship in 2001. The author is also very grateful to

Andre Nepgen, Dr. Motodi M., Johan Strydom, and Dr. Ajith Gopal for pro-

viding him the opportunity for sabbatical at the CSIR (SA). The constant

technical support from several colleagues of the Flight Mechanics and

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