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Introduction to statistical signal processing with applications
1484
Book Reviews
cepts. The exercises at the end of each chapter will benefit the reader greatly; furthermore, these exercises expand upon the material covered in the respective chapters. The material is logically ordered, and contains plenty of examples covering important points that illustrate the given techniques. Each chapter ends with a summary, and with references that set the material of the chapter in context with the existing literature.
be processed to evaluate the power spectral density or statistical characteristics, trajectory information may be extracted, or filtering may be used to distinguish between the heartbeat of a mother and her unborn child. Fast, accurate numerical methods are used for all of these functions. In a real-time system the data may be used to control a system, or to perform realtime data analysis and display. Common to all of these processes is lots of arithmetic.
The basic techniques are discussed in sufficient detail to enable graduate students and practising engineers in different disciplines to acquire a background suitable for advanced study, and a better understanding of the fields in which they are involved. The background required to use this book is a course on system theory and a course on stochastic processes. It is not a book for the uninitiated!
DSP hardware has been optimized to perlorm highspeed arithmetic very efficiently. Most DSP hardware allows simultaneous data acquisition and algorithm processing, and most software packages contain instruction sets that allow for parallel instructions to reduce the overhead associated with evaluating the repetitive arithmetic. The resulting hardware/software combination is not only fast, but also opens up entirely new areas which were once impossible to develop due to restrictions imposed by analog equipment; digital filters with vertical transition bands are possible and edge detection can be used for image recognition. Since the signal processing is implemented in code, a reduced turn-around time is possible on changes and new designs.
In conclusion, this is a well-written and very comprehensive book that provides the reader with the sophistication and depth of statistical signal processing and applications in communication and radar systems. It will be attractive to the specialists in the field, graduate students, and other specialists interested in statistical signal processing. I heartily recommend it to these readers.
C Language Algorithms for Digital Signal Processing, by Paul M. EMBREE and Bruce KIMBLE. Prentice Hail; Englewood Cliffs, NJ, USA; 1991; xvi + 459 pp.; $73; ISBN: 0-13-133406-9
Practical DSP Modeling, Techniques, and Programming in C, by Don MORGAN. John Wiley and Sons; New York, USA; 1994; xv + 432 pp.; $49.95; ISBN: 0-471-00434-0
The increasingly important role of DSP has also shown the advantages of the C language over other languages, such as FORTRAN and Pascal, for both computationally intensive and real-time software. C is used extensively in both these books, and the second book also makes use of some 80X86 assembly language. Both of the books are application-oriented, but they have slightly different intentions and are directed at different readers. The C Language Algorithms for Digital Signal Processing book is presented to engineers who are
Embry-Riddle Aeronautical University, Daytona Beach, FL, USA
familiar with digital signal processing and C, but are not experts in either, and who need to develop a particular DSP application. The book is also targeted at students and application-oriented programmers who need a quick source of debugged programs.
The continued rapid development of ever more powerful, smaller and cheaper microcomputers has led to faster and more widespread implementation than had ever been expected. A modern luxury car has more computing power than the first home computers that we all spent so many hours sweating over. We now live in the 'information age', and for engineers and scientists this often means digital information that must be analyzed, processed and used, frequently in real time. Digital signal processing (DSP) is so widely used for testing, implementation and control, that many types of engineers who would not traditionally be exposed to DSP are now required to work with it.
In reality, enough C is presented to allow anyone with structured programming experience to follow the code without difficulty. For the intended readers, the first two chapters should represent a review of DSP fundamentals and C. If the reader is at all fuzzy on fundamentals such as the z-transform, the relationship to the frequency response and the Fourier transform, the first chapter is required reading, and the references at the end of the chapter will be put to good use. Also introduced in the first chapter are finite impulse response (FIR) and infinite impulse response (IIR) filters, two-dimensional signal processing, nonlinear operators, random processes, correlations, Wiener filter theory and quantization noise.
DSP can mean off-line data processing in a non-realtime situation for analytical purposes. The data might
The general layout of the remainder of the book is easy to follow, with each section containing an intro-
Reviewed by: Jeroen H. DOLMANS
Book Reviews
cepts. The exercises at the end of each chapter will benefit the reader greatly; furthermore, these exercises expand upon the material covered in the respective chapters. The material is logically ordered, and contains plenty of examples covering important points that illustrate the given techniques. Each chapter ends with a summary, and with references that set the material of the chapter in context with the existing literature.
be processed to evaluate the power spectral density or statistical characteristics, trajectory information may be extracted, or filtering may be used to distinguish between the heartbeat of a mother and her unborn child. Fast, accurate numerical methods are used for all of these functions. In a real-time system the data may be used to control a system, or to perform realtime data analysis and display. Common to all of these processes is lots of arithmetic.
The basic techniques are discussed in sufficient detail to enable graduate students and practising engineers in different disciplines to acquire a background suitable for advanced study, and a better understanding of the fields in which they are involved. The background required to use this book is a course on system theory and a course on stochastic processes. It is not a book for the uninitiated!
DSP hardware has been optimized to perlorm highspeed arithmetic very efficiently. Most DSP hardware allows simultaneous data acquisition and algorithm processing, and most software packages contain instruction sets that allow for parallel instructions to reduce the overhead associated with evaluating the repetitive arithmetic. The resulting hardware/software combination is not only fast, but also opens up entirely new areas which were once impossible to develop due to restrictions imposed by analog equipment; digital filters with vertical transition bands are possible and edge detection can be used for image recognition. Since the signal processing is implemented in code, a reduced turn-around time is possible on changes and new designs.
In conclusion, this is a well-written and very comprehensive book that provides the reader with the sophistication and depth of statistical signal processing and applications in communication and radar systems. It will be attractive to the specialists in the field, graduate students, and other specialists interested in statistical signal processing. I heartily recommend it to these readers.
C Language Algorithms for Digital Signal Processing, by Paul M. EMBREE and Bruce KIMBLE. Prentice Hail; Englewood Cliffs, NJ, USA; 1991; xvi + 459 pp.; $73; ISBN: 0-13-133406-9
Practical DSP Modeling, Techniques, and Programming in C, by Don MORGAN. John Wiley and Sons; New York, USA; 1994; xv + 432 pp.; $49.95; ISBN: 0-471-00434-0
The increasingly important role of DSP has also shown the advantages of the C language over other languages, such as FORTRAN and Pascal, for both computationally intensive and real-time software. C is used extensively in both these books, and the second book also makes use of some 80X86 assembly language. Both of the books are application-oriented, but they have slightly different intentions and are directed at different readers. The C Language Algorithms for Digital Signal Processing book is presented to engineers who are
Embry-Riddle Aeronautical University, Daytona Beach, FL, USA
familiar with digital signal processing and C, but are not experts in either, and who need to develop a particular DSP application. The book is also targeted at students and application-oriented programmers who need a quick source of debugged programs.
The continued rapid development of ever more powerful, smaller and cheaper microcomputers has led to faster and more widespread implementation than had ever been expected. A modern luxury car has more computing power than the first home computers that we all spent so many hours sweating over. We now live in the 'information age', and for engineers and scientists this often means digital information that must be analyzed, processed and used, frequently in real time. Digital signal processing (DSP) is so widely used for testing, implementation and control, that many types of engineers who would not traditionally be exposed to DSP are now required to work with it.
In reality, enough C is presented to allow anyone with structured programming experience to follow the code without difficulty. For the intended readers, the first two chapters should represent a review of DSP fundamentals and C. If the reader is at all fuzzy on fundamentals such as the z-transform, the relationship to the frequency response and the Fourier transform, the first chapter is required reading, and the references at the end of the chapter will be put to good use. Also introduced in the first chapter are finite impulse response (FIR) and infinite impulse response (IIR) filters, two-dimensional signal processing, nonlinear operators, random processes, correlations, Wiener filter theory and quantization noise.
DSP can mean off-line data processing in a non-realtime situation for analytical purposes. The data might
The general layout of the remainder of the book is easy to follow, with each section containing an intro-
Reviewed by: Jeroen H. DOLMANS