Hidden Markov Models: Estimation and Control
by R. J. Elliott, L. Aggoun, J. B. Moore
Publisher: Springer 1995
Number of pages: 373
The aim of this book is to present graduate students with a thorough survey of reference probability models and their applications to optimal estimation and control. These new and powerful methods are particularly useful in signal processing applications where signal models are only partially known and are in noisy environments.
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by Salih Mohammed Salih (ed.) - InTech
The book covers fast hybrid recursive FT based on Jacket matrix, acquisition algorithm for global navigation system, determining the sensitivity of output parameters based on FFT, convergence of integrals based on Riemann-Lebesgue Lemma function, ...
- Agilent Technologies
This text is a primer for those who are unfamiliar with the advantages of analysis in the frequency and modal domains and Dynamic Signal Analyzers. The authors avoid the use of rigorous mathematics and instead depend on heuristic arguments.
by John Treichler - Connexions
This book examines how to convert a typical filter specification into a reasonably accurate estimate of the length of the impulse response. The text covers filter sizing, performance comparisons with other FIR design methods, etc.
by G. Larry Bretthorst - Springer
This work is a research document on the application of probability theory to the parameter estimation problem. The people who will be interested in this material are physicists, economists, and engineers who have to deal with data on a daily basis.