Statistical and Adaptive Signal Processing

Some subjects are more difficult to master than others. Learning Statistical and Adaptive Signal Processing (SASP), for example, is not as easy as learning to program in C. And that's why it's important to get a good book. Almost any decent book will work for learning C. Most of the subject matter is straight forward for an intelligent person. Unless you're brilliant, learning advanced DSP is going to involve reading and rereading the material. You're going to be spending a lot of time with the text book you choose.

The book mentioned above was lent to me by my advisor. I would go so far as to say that it was the best text book I have ever used. Because my school was an undergraduate school, there were no courses in adaptive DSP. It's too specific a field. The result was I had to learn most of the material on my own. I spent the first month or so reading a number of chapters out of SASP. Even two years later when I was finishing up my work, I was still going back to that book when I needed to review material or learn something new. The explanations are excellent. It does a good job of giving you an overview, yet still going in depth into many topics. It coverd enough material in echo cancellation to get me to the point where I could read and understand some of the latest research.

I was surprised that it received a number of poor reviews on Amazon. Perhaps DSP books tend to be better than other text books, but I don't think that's true. Or perhaps the reviewers haven't had to use other text books. If you're interested in the subject matter, this book won't put you to sleep. It might not be a page turner like Ender's Game, but you won't find yourself mindlessly reading the pages.

You will learn about statistical processing functions, optimum filtering, filtering in adaptive environments, estimation theory, array theory, and there's even an introduction to space-time processing. There are good discussions on algorithm derivations, comparisons and interpretations of different techniques, fast matrix algorithms, and applications. Some chapters go into heavy performance analysis. This is where some of the real learning happens. You need to understand the basic algorithms and their limitations before you can study the advanced algorithms. It will be well worth the money.

I suggest you read the first few chapters and try to understand them the best you can. If there is a specific topic you need to learn, you should then be able to jump to almost any chapter in the book. Some chapters do build on others; so, you may find that you need to refer back to earlier chapters. But don't worry, you'll only need to read a few pages in most cases, not the entire chapter. Try implementing the algorithm you want to learn. That's the best way to understand. Pay careful attention to subscripts, dimensions, and bolding. There are a lot of subscripts and superscripts in DSP algorithms. Knowing what they mean is half the battle. SASP is very good about remaining consistent throughout the book. It also adopts some of the notational conventions that have evolved over the past two to three decades. Learn those conventions.