E-Books → Detection, Estimation, and Modulation Theory - Part l - Detection, Estimation, and Linear Modulation Theory

Published by: voska89 on 22-07-2022, 08:28 | 0

Detection, Estimation, and Modulation Theory - Part l - Detection, Estimation, and Linear Modulation Theory By Van Trees, Harry L.
2001 | 732 Pages | ISBN: 0471095176 | PDF | 14 MB
Written in the same accessible style as Van Tree's earlier classics, this completely new work covers all modern applications of array signal processing, from biomedicine to wireless communications. Highly readable paperback reprint of one of the great time-tested classics in the field of signal processing Together with the reprint of Part III and the new Part IV, this will be the most complete treatment of the subject available As imperative today as it was when it originally published Has important applications in radar, sonar, communications, seismology, biomedical engineering, and astronomy Includes section summaries, examples, and a large number of problems

E-Books → Hidden Markov Models Estimation and Control

Published by: voska89 on 21-07-2022, 14:23 | 0

Hidden Markov Models: Estimation and Control By Robert J. Elliott, John B. Moore, Lakhdar Aggoun (auth.)
1995 | 382 Pages | ISBN: 0387943641 | PDF | 6 MB
As more applications are found, interest in Hidden Markov Models continues to grow. Following comments and feedback from colleagues, students and other working with Hidden Markov Models the corrected 3rd printing of this volume contains clarifications, improvements and some new material, including results on smoothing for linear Gaussian dynamics. In Chapter 2 the derivation of the basic filters related to the Markov chain are each presented explicitly, rather than as special cases of one general filter. Furthermore, equations for smoothed estimates are given. The dynamics for the Kalman filter are derived as special cases of the authors' general results and new expressions for a Kalman smoother are given. The Chapters on the control of Hidden Markov Chains are expanded and clarified. The revised Chapter 4 includes state estimation for discrete time Markov processes and Chapter 12 has a new section on robust control.

E-Books → Optimal and Robust State Estimation Finite Impulse Response (FIR) and Kalman Approaches

Published by: voska89 on 20-07-2022, 08:53 | 0

English | 2022 | ISBN: 1119863074, 978-1119863076 | 483 pages | True PDF | 9.26 MB
A unified and systematic theoretical framework for solving problems related to finite impulse response (FIR) estimate
Optimal and Robust State Estimation: Finite Impulse Response (FIR) and Kalman Approaches is a comprehensive investigation into batch state estimators and recursive forms. The work begins by introducing the reader to the state estimation approach and provides a brief historical overview. Next, the work discusses the specific properties of finite impulse response (FIR) state estimators. Further chapters give the basics of probability and stochastic processes, discuss the available linear and nonlinear state estimators, deal with optimal FIR filtering, and consider a limited memory batch and recursive algorithms.

E-Books → Shmaliy Y Optimal and Robust State Estimation 2022

Published by: Emperor2011 on 17-07-2022, 18:11 | 0

Shmaliy Y Optimal and Robust State Estimation 2022 | 9.26 MB
English | 483 Pages

Title: Optimal and Robust State Estimation
Author: Shmaliy, Yuriy S.;Zhao, Shunyi;
Year: 2022

E-Books → Optimal Input Signals for Parameter Estimation

Published by: voska89 on 21-06-2022, 10:33 | 0

English | 2022 | ISBN: 3110350890 | 203 pages | True PDF | 6.76 MB
The aim of this book is to provide methods and algorithms for the optimization of input signals so as to estimate parameters in systems described by PDE's as accurate as possible under given constraints. The optimality conditions have their background in the optimal experiment design theory for regression functions and in simple but useful results on the dependence of eigenvalues of partial differential operators on their parameters. Examples are provided that reveal sometimes intriguing geometry of spatiotemporal input signals and responses to them.