| Filter Design Toolbox | |
| Filter Design Functions in the Toolbox |
Outlines the filter design functions available in the toolbox |
| Quantization Functions in the Toolbox |
Outlines the quantization functions available in the toolbox |
| Comparison to the Signal Processing Toolbox |
Explains where the toolbox differs from the Signal Processing Toolbox--the different and more advanced features |
| Getting Started with the Toolbox |
Provides an introduction to the toolbox by presenting examples that design various filters |
| Selected Bibliography |
Lists some books that offer details about digital filtering and digital signal processing |
When you install Filter Design Toolbox in your MATLAB® environment, you can perform digital filter design, fixed- and floating-point filter quantization, and filter performance analysis on your desktop computer. But what are filtering and quantization and what benefits do they provide?
Designers use filtering and its variant, digital filtering, for many tasks:
You can use analog filters to accomplish these tasks, but digital filters offer greater flexibility and accuracy than analog filters. In addition, digital signal processing (DSP) depends in large measure on digital filtering to meet the needs of its users.
Analog filters can be cheaper, faster, and have greater dynamic range; digital filters outstrip their analog cousins in flexibility. The ability to create filters that have arbitrary shape frequency response curves, and filters that meet performance constraints, such as bandpass width and transition region width, is well beyond that of analog filters.
Quantization is a natural outgrowth of digital filtering and digital signal processing development. Also, there is a growing need for fixed-point filters that meet power, cost, and size restrictions. When you convert a filter from floating-point to fixed-point, you use quantization to perform the conversion.
As filter designers began to use digital filters in applications where power limitations and size constraints drove the filter design, they moved from double-precision, floating-point filters to fixed-point filters. When you have enough power to run a floating-point digital signal processor, such as on
desktop PC or in your car, fixed-point processing and filtering are unnecessary. But, when your filter needs to run in a cellular phone, or you want to run a hearing aid for hours instead of seconds, fixed-point processing can be essential to ensure long battery life and small size.
Filter Design Toolbox provides the functions you need to develop filters that meet the needs of fixed-point algorithms and electronics systems. In addition to offering tools for analyzing the effects of quantization on filter performance and signal processing performance, the toolbox offers filter structures for you to use to develop prototype filter designs. With structures ranging from finite impulse response (FIR) filters to infinite impulse response (IIR) filters, you can investigate alternative fixed-point realizations of filters that might meet your goals.
This section contains the following subsections introducing filter design:
| | Typographical Conventions | Filter Design Functions in the Toolbox | |