Spectral Analysis

Parametric and nonparametric methods

The 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

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Non-Parametric Estimation

`spectrumAnalyzer`	Display frequency spectrum of time-domain signals (Since R2022a)
`dsp.SpectrumEstimator`	Estimate power spectrum or power density spectrum
`dsp.CrossSpectrumEstimator`	Estimate cross-spectral density
`dsp.TransferFunctionEstimator`	Estimate transfer function

Blocks

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Non-Parametric Estimation

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

Parametric Estimation

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 spectrumAnalyzer and the dsp.SpectrumEstimator objects.
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 shows how to perform high resolution spectral analysis by using an efficient polyphase filter bank sometimes 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

Filter Bank Method: A Better Approach to Spectral Analysis

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.

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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.

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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.

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