Fundamentals of Stochastic Signals, Systems and Estimation Theory : With Worked Examples （2ND）

Kovacevic, B./ Durovic, Z.

Springer（2008/06発売）

• 製本 Hardcover:ハードカバー版／ページ数 380 p.
• 言語 ENG
• 商品コード 9783540709909
• DDC分類 620

Full Description

This book is intended for undergraduate students, graduate students and practical engineers, specializing in the areas ofelectrical communications, signalprocessing and automatic control. It has emergedfrom various lecture notes, in Serbian and in English, that we have compiledand used in different courses at Faculty ofElectrical Engineering, University ofBelgrade, since 1980. The specializing in the areas of electrical communications, signal processing and automatic control required background is a course in a probability theory and basic linear dynamic systems and signals theory (transferfunctions and state space). Appendix A provides a short review ofLaplace and Z-transformfor those who need it. Moreover, one advantageofmodern control theory is that it employs matrix algebra, which results in a simplification in notation and mathematical manipulations when dealing with multivariable systems. Therefore, a brief review of the matrix algebra is presented in Appendix B. Generally speaking, the goal ofthis textbook is to clarify the terms digital filtering, Kalman filtering and other types ofestimation techniques appearing quite often in the present-day professional literature.
In the electrical engineering curriculum, a course in Stochastic signals, systems and estimation is introduced to accompany underlying courses in Control systems, Communication systems and Signal processing. In addition, many problems in wireless communications, networking, electronics, photonics, power systems and robotics are now studiedfrom the stochastic signals and systems point of view. This textbook can be also used by other engineering students interested in these topics, especially biomedical, aerospace, civil, traffic, mechanicaland industrial engineering students.

Contents

Review of The Theory of Probability and Random Variables.- Fundamentals of Stochastic Processes.- Linear Discrete-Time Stochastic Systems.- Linear Continuous Time Stochastic Systems.- Fundamentals of Estimation.- Optimum Nonrecursive Linear Estimation: Wiener Filtering.- Optimum Recursive Linear Estimation: Kalman Filtering.- Extensions of The Optimum Recursive (Kalman) Filter.