| Fixed-Point Blockset | |
Overview
This chapter investigates how you can realize digital filters using the Fixed-Point Blockset.
The Fixed-Point Blockset addresses the needs of the control system and signal processing fields, and other fields where algorithms are implemented on fixed-point hardware. In signal processing, a digital filter is a computational algorithm that converts a sequence of input numbers to a sequence of output numbers. The algorithm is designed such that the output signal meets frequency-domain or time-domain constraints (desirable frequency components are passed, undesirable components are rejected).
In general terms, a discrete transfer function controller is a form of a digital filter. However, a digital controller may contain nonlinear functions such as look-up tables in addition to a discrete transfer function. This guide uses the term digital filter when referring to discrete transfer functions.
Realizations and Data Types
In an ideal world where numbers, calculations, and storage of states have infinite precision and range, there are virtually an infinite number of realizations for the same system. In theory, these realizations are all identical to each other.
In the more realistic world of double-precision numbers, calculations, and storage of states, small nonlinearities are introduced due to the finite precision and range of floating-point data types. Therefore, each realization of a given system produces different results. In most cases however, these differences are small.
In the world of fixed-point numbers where precision and range are limited, the differences in the realization results can be very large. Therefore, you must carefully select the data type, word size, and scaling for each realization element such that results are accurately represented. To assist you with this selection, design rules for modeling dynamic systems with fixed-point math are provided in Targeting an Embedded Processor.
| | Realization Structures | Targeting an Embedded Processor | |