Spectral Analysis
R2026bThe frequency-domain representation of a signal reveals important
signal characteristics that are difficult to analyze in the time domain.
Spectral analysis lets you characterize the frequency content of a
signal. Perform real-time spectral analysis of a dynamic signal using
the spectrumAnalyzer object in MATLAB® and the Spectrum Analyzer block in
Simulink®. The Spectrum Analyzer uses the filter bank method or the
Welch's method of averaging modified periodogram to compute the spectral
data. Both these methods are FFT-based spectral estimation methods that
make no assumptions about the input data and can be used with any kind
of signal. For more information on the algorithm the Spectrum Analyzer
uses, see Spectral Analysis. In addition
to viewing the spectrum, you can also view the spectrogram of the signal
in the Spectrum Analyzer. For an example, see View the Spectrogram Using Spectrum Analyzer.
If you want to acquire this data for post processing in MATLAB, call isNewDataReady and getSpectrumData object functions on the Spectrum
Analyzer object. By calling these functions in the streaming loop, you
can acquire the entire spectral data. In Simulink, to acquire the spectral data, create a SpectrumAnalyzerBlockConfiguration object and run the
getSpectrumData function on this object. Note that in
Simulink, you can acquire only the last frame of the spectral data
shown on the Spectrum Analyzer.
Alternately, you can use the dsp.SpectrumEstimator
System object™ and Spectrum Estimator block to
compute the power spectrum and acquire the spectral data for further
processing. To view the spectral data computed by the spectrum
estimator, use an array plot. For examples, see Estimate the Power Spectrum in MATLAB and Estimate the Power Spectrum in Simulink.
Objects
Blocks
 | Burg Method | Power spectral density estimate using Burg method |
 | Covariance Method | Power spectral density estimate using covariance method |
 | Cross-Spectrum Estimator | Estimate cross-power spectrum density |
 | Discrete Transfer Function Estimator | Compute estimate of frequency-domain transfer function of system |
 | Magnitude FFT | Compute nonparametric estimate of spectrum using periodogram method |
 | Modified Covariance Method | Power spectral density estimate using modified covariance method |
 | Periodogram | Power spectral density or mean-square spectrum estimate using periodogram method |
 | Short-Time FFT | Nonparametric estimate of spectrum using short-time fast Fourier transform (STFT) method |
 | Spectrum Analyzer | Display frequency spectrum |
 | Spectrum Estimator | Estimate power spectrum or power-density spectrum |
 | Yule-Walker Method | Power spectral density estimate using Yule-Walker method |
 | Burg AR Estimator | Compute estimate of autoregressive (AR) model parameters using Burg method |
 | Covariance AR Estimator | Compute estimate of autoregressive (AR) model parameters using covariance method |
 | Modified Covariance AR Estimator | Compute estimate of autoregressive (AR) model parameters using modified covariance method |
 | Yule-Walker AR Estimator | Compute estimate of autoregressive (AR) model parameters using Yule-Walker method |
Topics
- Spectral Analysis
Spectral analysis is the process of estimating the power spectrum (PS) of a signal from its time-domain representation.
- Estimate the Power Spectrum in MATLAB
Compute the power spectrum using the
spectrumAnalyzerand thedsp.SpectrumEstimatorobjects. - Estimate the Power Spectrum in Simulink
Compute the power spectrum using the Spectrum Analyzer and the Spectrum Estimator blocks.
- Streaming Power Spectrum Estimation Using Welch's Method
Use Welch's method of averaged modified periodogram to estimate power spectrum.
- High Resolution Filter-Bank-Based Power Spectrum Estimation
This example demonstrates how to perform high-resolution spectral analysis using an efficient polyphase filter bank, commonly referred to as a Channelizer.
- View the Spectrogram Using Spectrum Analyzer
Spectrograms are a two-dimensional representation of the power spectrum of a signal as this signal sweeps through time.
- 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.
- Continuous-Time Transfer Function Estimation
This example shows how to use the Discrete Transfer Function Estimator block to estimate the magnitude and phase response of a continuous-time analog filter.
- Group Delay Estimation in Simulink
This example shows how to estimate the group delay of a filter in Simulink®.
- Variable-Size Signal Support DSP System Objects
List of System objects that support variable-sized signals in DSP System Toolbox™.
Related Information
Featured Examples
High Resolution Spectral Analysis in MATLAB
Perform spectral estimation in MATLAB using the filter bank method, and compare the performance with the Welch's averaged modified periodogram method.
High Resolution Spectral Analysis in Simulink
Perform spectral estimation in Simulink using the filter bank method, and compare the performance with the Welch's averaged modified periodogram method.
Spectrum Analyzer Measurements
Use the Spectrum Analyzer block for harmonic distortion measurements (such as THD, SNR, SINAD, and SFDR), third-order intermodulation (TOI) distortion measurements, and adjacent channel power ratio (ACPR) measurements. The example also shows you how to view time-varying spectra by using a spectrogram and automatic peak detection. The example includes five amplifier models, with each model representing a typical setup to perform one of the measurements.
