Learn more about Signal Processing Toolbox

Central Features

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Signal Processing Toolbox Functions Signals and Systems Filter Design, Analysis, and Implementation Linear System Transformations Windowing Functions Spectral Analysis Transforms Statistical Signal Processing Parametric Modeling Linear Prediction Multirate Signal Processing Waveform Generation Other Operations

Signal Processing Toolbox Functions

Signal Processing Toolbox functions are algorithms, expressed mostly in M-files, that implement a variety of signal processing tasks. These toolbox functions are a specialized extension of the MATLAB computational and graphical environment.

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Signals and Systems

The basic entities that toolbox functions work with are signals and systems. The functions emphasize digital (or discrete) signals and filters, as opposed to analog (or continuous) signals. The principal filter type the toolbox supports is the linear, time-invariant digital filter with a single input and a single output. You can represent linear time-invariant systems using one of several models (such as transfer function, state-space, zero-pole-gain, and second-order section), and you can convert between representations.

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Filter Design, Analysis, and Implementation

Signal Processing Toolbox software provides customizable support for filter design. The major filter design functions included in the toolbox are FIR and IIR filter design, analysis, and implementation, filter order estimation, and analog filter prototyping and transformations.

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Linear System Transformations

The toolbox has a number of transformation functions, including conversions to and from second-order sections, state-space, pole-zero, lattice or ladder, and transfer functions.

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Windowing Functions

The toolbox provides many commonly used window functions as well as graphical user interfaces to view and compare windows and design filters using these windows.

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Spectral Analysis

Toolbox functions are available for estimating the power spectral density, mean-square spectral estimate, and pseudo spectrum, using parametric and nonparametric techniques. Some of the spectral analysis methods included in the toolbox are Burg, covariance, eigenvector, Thomson multitaper, periodogram, Welch, and Yule-Walker. Other functions are available for computing the average power of a power spectral density, computing a one-sided spectrum, and shifting the DC component to the center of a spectrum.

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Transforms

The toolbox includes a variety of transforms and inverse transforms, including the Fourier, chirp-Z, discrete cosine, Goertzel, Hilbert, and Walsh-Hadamard.

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Statistical Signal Processing

The toolbox has functions for computing correlation, cross-correlation, covariance, and autocorrelation.

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Parametric Modeling

The toolbox includes these methods for autoregressive parametric modeling: Burg, covariance, Yule-Walker, and Steiglitz-McBride (for ARMA modeling). The toolbox also has functions for fitting a frequency response to an analog or discrete-time filter.

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Linear Prediction

The toolbox has functions for computing linear prediction coefficients and for converting between autorcorrelations and prediction polynomials, reflection coefficients, and line spectral frequencies.

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Multirate Signal Processing

The toolbox includes a number of functions for multirate signal processing, including decimation, up- and downsampling, resampling, and spline interpolation.

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Waveform Generation

The toolbox has functions to generate many types of periodic and aperiodic waveforms, including chirp, Dirichlet function, Gaussian RF pulse, Gaussian monopulse, pulse train, rectangle, sawtooth, sinc, square wave, triangle, and voltage-controlled oscillator. See Waveform Generation: Time Vectors and Sinusoids for more information.

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Other Operations

A number of other operations are also available in the toolbox. Some of these are cepstral analysis, modulation, demodulation, Slepian sequences, and various plotting methods.

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Product Overview

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