DSP System Toolbox™ provides algorithms, apps, and scopes for designing, simulating, and analyzing signal processing systems in MATLAB® and Simulink®. You can model real-time DSP systems for communications, radar, audio, medical devices, IoT, and other applications.
With DSP System Toolbox you can design and analyze FIR, IIR, multirate, multistage, and adaptive filters. You can stream signals from variables, data files, and network devices for system development and verification. The Time Scope, Spectrum Analyzer, and Logic Analyzer let you dynamically visualize and measure streaming signals. For desktop prototyping and deployment to embedded processors, including ARM® Cortex® architectures, the toolbox supports C/C++ code generation. It also supports bit-accurate fixed-point modeling and HDL code generation from filters, FFT, IFFT, and other algorithms.
Algorithms are available as MATLAB functions, System objects, and Simulink blocks.
Introduction to Streaming Signal Processing in MATLAB
This example shows how to use System objects to do streaming signal processing in MATLAB.
Filter Frames of a Noisy Sine Wave Signal in MATLAB
This example shows how to lowpass filter a noisy signal in MATLAB and visualize the original and filtered signals using a spectrum analyzer.
Filter Frames of a Noisy Sine Wave Signal in Simulink
This example shows how to lowpass filter a noisy signal in Simulink and visualize the original and filtered signals with a spectrum analyzer.
Lowpass Filter Design in MATLAB
This example shows how to design lowpass filters.
Tunable Lowpass Filtering of Noisy Input in Simulink
This example shows how to filter a noisy chirp signal with a lowpass filter that has a tunable passband frequency.
If you are using R2016a or an earlier release, replace each call to the object with the equivalent step syntax.
Signal Processing Acceleration through Code Generation
Accelerate signal processing algorithm with codegen and
dspunfold.
Estimate the Power Spectrum in MATLAB
Compute the power spectrum using the dsp.SpectrumAnalyzer and
the dsp.SpectrumEstimator System objects.
Estimate the Transfer Function of an Unknown System
You can estimate the transfer function of an unknown system based on the system's measured input and output data.
View the Spectrogram Using Spectrum Analyzer
Compute the spectrogram and show the effect of RBW on the spectral data.
Signal Visualization and Measurements in MATLAB
This example shows how to visualize and measure signals in the time and frequency domain in MATLAB using a time scope and spectrum analyzer.
Obtain Measurement Data Programmatically for dsp.SpectrumAnalyzer System object
Obtain measurements data from dsp.SpectrumAnalyzer System object.
Obtain Measurements Data Programmatically for Spectrum Analyzer Block
Obtain measurements data from Spectrum Analyzer block.
Fixed-Point Filter Design in MATLAB
This example shows how to design filters for use with fixed-point input.
Generate DSP Applications with MATLAB Compiler
This example shows how to use the MATLAB Compiler™ to create a standalone application from a MATLAB function that uses System objects from DSP System Toolbox™.
Generate Standalone Executable And Interact With It Using UDP
This example shows how to generate a standalone executable for streaming statistics using MATLAB Coder™ and tune the generated executable using a user interface (UI) that is running in MATLAB (TM).
Code Generation for Parametric Audio Equalizer
This example shows how to model an algorithm specification for a three band parametric equalizer which will be used for code generation.
Programmable FIR Filter for FPGA
This example shows how to implement a programmable FIR filter for hardware.
Shows how to configure the Simulink environment for use in signal processing models.
Introduction to real-world sample- and frame-based signals, and how to model those signals in MATLAB and Simulink.
Configure the Simulink environment to minimize delay and increase simulation performance.
Create a variable-size signal whose size, values cam change during a simulation.
As you construct a model you can experiment with block parameters, such as the coefficients of a Transfer Fcn block, to help you decide which blocks to use.
Discusses advantages of fixed-point development in general and of fixed-point support in System Toolbox software in particular, as well as lists common applications of fixed-point signal processing development.