Linear Prediction Theory
Peter Strobach · 1990
Where to buy
Amazon’s price changes daily, so the most accurate place to see it is there, at the moment you buy.
View on AmazonThis is an affiliate link: if you buy through it, the site earns a small commission at no extra cost to you, which helps us bring quality content and maintain the site. As an Amazon Associate we earn from qualifying purchases.
About the book
The theory presented in this book forms the basis of many algorithms for parameter estimation, adaptive system identification, and adaptive filtering. Linear prediction theory has applications in such fields as communications, control, radar and sonar systems, geophysics, estimation of economic processes, and training problems in synthetic neural nets. Emphasis is placed on three main areas. First, the mathematical tools required for the most important linear prediction algorithms are derived in a unified framework. Second, the relationships between different approaches are pointed out, thus allowing the selection of the optimal technique for a particular problem. Third, the material is presented in the context of the latest results of algorithm research, with many references to recent publications in the field. The book is suitable for a graduate course on adaptive signal processing and will be useful for practising engineers faced with the problem of designing systems for operation in time-varying environments.
Book details
- Year
- 1990
- Language
- English
- Series
- Springer Series in Information Sciences -- 21
- Publisher
- Springer Berlin Heidelberg
- Editions
- 2
- Pages
- 422
- ISBN
- 9783642752087
Data source: Wikidata