Random Processes: Filtering, Estimation, and Detection (Hardcover)

An understanding of random processes is crucial to many engineering fields-including communication theory, computer vision, and digital signal processing in electrical and computer engineering, and vibrational theory and stress analysis in mechanical engineering. The filtering, estimation, and detection of random processes in noisy environments are critical tasks necessary in the analysis and design of new communications systems and useful signal processing algorithms. Random Processes: Filtering, Estimation, and Detection clearly explains the basics of probability and random processes and details modern detection and estimation theory to accomplish these tasks.
In this book, Lonnie Ludeman, an award-winning authority in digital signal processing, joins the fundamentals of random processes with the standard techniques of linear and nonlinear systems analysis and hypothesis testing to give signal estimation techniques, specify optimum estimation procedures, provide optimum decision rules for classification purposes, and describe performance evaluation definitions and procedures for the resulting methods. The text covers four main, interrelated topics:
* Probability and characterizations of random variables and random processes
* Linear and nonlinear systems with random excitations
* Optimum estimation theory including both the Wiener and Kalman Filters
* Detection theory for both discrete and continuous time measurements
Lucid, thorough, and well-stocked with numerous examples and practice problems that emphasize the concepts discussed, Random Processes: Filtering, Estimation, and Detection is an understandable and useful text ideal as both a self-study guide for professionals in the field and as a core text for graduate students.

Table of Contents

Preface.

Experiments and Probability.

Random Variables.

Estimation of Random Variables.

Random Processes.

Linear Systems: Random Processes.

Nonlinear Systems: Random Processes.

Optimum Linear Filters: The Wiener Approach.

Optimum Linear Systems: The Kalman Approach.

Detection Theory: Discrete Observation.

Detection Theory: Continuous Observation.

Appendix A. The Bilateral Laplace Transform.

Appendix B. Table of Binomial Probabilities.

Appendix C. Table of Discrete Random Variables and Properties.

Appendix D. Table of Continuous Random Variables and Properties.

Appendix E. Table of Gaussian Cumulative Distribution Function.

Index.