Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory

Rating:
Summary: Great, but a tad lacking
Review: I am an undergraduate senior using this book for a graduate-level Detection & Estimation Theory course. This is an excellent book and is very easy to follow, unlike Poor's which is too mathematical and hard too read. However, this book does make many references to Vol. 1, the Estimation Theory, so you almost have to get both books to get a full understanding. Also, some of the problems are not always workable based on the theory given. Overall, though, I do recommend this book.

Rating:
Summary: Great, but a tad lacking
Review: I am an undergraduate senior using this book for a graduate-level Detection & Estimation Theory course. This is an excellent book and is very easy to follow, unlike Poor's which is too mathematical and hard too read. However, this book does make many references to Vol. 1, the Estimation Theory, so you almost have to get both books to get a full understanding. Also, some of the problems are not always workable based on the theory given. Overall, though, I do recommend this book.

Rating:
Summary: The best textbook in Estimation Theory
Review: Steven Kay has done a superb job. His coverage of different aspects of estimation theory make this book an excellent reference for the working engineer, as well as a great college textbook. All the topics are well covered, there are meaningful examples that apply the theory to different aspects of signal processing, and the problem sets are very useful. Also, the addition of small appendixes to the end of some chapters is very unique. They usually contain additional material to clarify a method presented in the chapter or discuss some mathematical result. A book that can balance theory with practical applications is not easy to find, especially in a difficult area like statistical signal processing. This is the ultimate guide to estimation theory and its applications.

Rating:
Summary: If you want to learn detection theory get this book.
Review: This book presents the fundamental ideas in detection theory (Hypothesis testing for you math types) in a very accessible way. It was one of the few textbooks I could read without feeling like I should be taking a class to go along with it. First the book starts off with a short chapter explaining what detection theory is. This chapter is so well written and illustrated that I've used it to explain to my friends what it is I do at work. This type of very good introduction is repeated at the start of each chapter. But I would have to say the strength of the book is in the examples. All the main ideas are demonstrated by examples that have been worked out in detail. There are several of these worked out examples per chapter and they range form simple, to the kinds of problems you may face in real life. I keep this book on my desk at work, and on more than on occasion I looked liked a genius by using this book and it's examples to solve a problem in minuets that looked like it would take hours (or days).

Rating:
Summary: If you want to learn detection theory get this book.
Review: This book presents the fundamental ideas in detection theory (Hypothesis testing for you math types) in a very accessible way. It was one of the few textbooks I could read without feeling like I should be taking a class to go along with it. First the book starts off with a short chapter explaining what detection theory is. This chapter is so well written and illustrated that I've used it to explain to my friends what it is I do at work. This type of very good introduction is repeated at the start of each chapter. But I would have to say the strength of the book is in the examples. All the main ideas are demonstrated by examples that have been worked out in detail. There are several of these worked out examples per chapter and they range form simple, to the kinds of problems you may face in real life. I keep this book on my desk at work, and on more than on occasion I looked liked a genius by using this book and it's examples to solve a problem in minuets that looked like it would take hours (or days).