MATLAB and Simulink for Signal Processing

 

MATLAB and Simulink for Signal Processing

Analyze signals and time-series data. Model, design, and simulate signal processing systems.

Signal processing engineers use MATLAB® and Simulink® at all stages of development—from analyzing signals and exploring algorithms to evaluating design implementation tradeoffs for building real-time signal processing systems. MATLAB and Simulink offer:

  • Built-in functions and apps for analysis and preprocessing of time-series data, spectral and time-frequency analysis, and signal measurements
  • Apps and algorithms to design, analyze, and implement digital filters (FIR and IIR) from basic FIR and IIR filters to adaptive, multirate, and multistage designs
  • An environment to model and simulate signal processing systems with a combination of programs and block diagrams
  • Capabilities to model fixed-point behavior and automatically generate C/C++ or HDL code for deploying on embedded processors, FPGAs, and ASICs
  • Tools for developing predictive models on signals and sensor data using machine learning and deep learning workflows

“I don’t think MATLAB has any strong competitors for signal processing and wavelet analysis. When you add in its statistics and machine learning capabilities, it’s easy to see why nonprogrammers enjoy using MATLAB, particularly for projects that require combining all these methods.”

Ali Bahrami Rad, Aalto University

Signal Analysis and Measurements

MATLAB and Simulink help you analyze signals using built-in apps for visualizing and preprocessing signals in time, frequency, and time-frequency domains to detect patterns and trends without having to manually write code. You can characterize signals and signal processing systems using domain-specific algorithms across different applications such as communications, radar, audio, medical devices, and IoT.

Filter Design and Analysis

Design and analyze digital filters from basic single-rate lowpass or highpass to more advanced FIR and IIR designs, including multirate, multistage, and adaptive filters. You can visualize magnitude, phase, group delay, and impulse response, as well as evaluate filter performance, including stability and phase linearity. Filter designs can be analyzed and simulated to evaluate the effects of different internal structures and fixed-point data types. They can also generate embedded software or hardware implementations. For advanced and application-specific use cases, you can exploit predesigned filters and filter banks, like wavelet-based filter banks, perceptually-spaced filter banks, or channelizers.

Model-Based Design for Signal Processing

When designing signal processing systems, you can use a combination of block diagrams and language-based programming. You can use Simulink to apply Model-Based Design to signal processing systems for modeling, simulation, early verification, and code generation. You can use libraries of blocks with application-specific algorithms for baseline signal processing, audio, analog mixed-signal and RF, wireline and wireless communications, and radar systems. You can visualize live signals during simulations using virtual scopes, including spectrum and logic analyzers, constellations, and eye diagrams.

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Embedded Code Generation

You can automatically generate C and C++ code from signal processing algorithms and bit-accurate system models using MATLAB Coder™ and Simulink Coder™. The generated code can be used for simulation acceleration, rapid prototyping, and embedded implementation of your system. You can also generate optimized C code for targeting embedded hardware processors such as ARM® Cortex®-A or Cortex-M.

You can also generate portable, synthesizable Verilog® and VHDL® code from MATLAB functions and Simulink models. The generated HDL code can be used for FPGA programming or ASIC design.

Machine and Deep Learning

With MATLAB, you can build predictive models for signal processing applications. You can exploit built-in signal processing algorithms to extract features for machine learning systems as well as work with large datasets for ingesting, augmenting, and annotating signals when developing deep learning applications.