Spectral analysis is key to understanding signal characteristics, and it can be applied across all signal types, including radar signals, audio signals, seismic data, financial stock data, and biomedical signals. Signal Processing Toolbox provides MATLAB functions for estimating the power spectral density, mean-square spectrum, pseudo spectrum, and average power of signals.
Algorithms for Spectral Analysis in MATLAB
Spectral estimation algorithms in the toolbox include:
• FFT-based methods, such as periodogram, Welch, and multitaper
• Parametric methods, such as Burg and Yule-Walker
• Eigen-based methods, such as eigenvector and multiple signal classification (MUSIC)
Visualization in the Frequency Domain
Spectral analysis functions in the toolbox enable you to compute and view a signal’s:
• Time-frequency representation of a signal using the spectrogram function
• Power spectral density
• Mean-square spectrum
Visualizing signal spectra obtained with spectral analysis methods in MATLAB. Example plots from Signal Processing Toolbox include (clockwise from top left): Spectrogram of clean and noisy audio signals; mean-square spectrum of A/D converter input and output signals with aliasing in the output; and power spectral density of a noisy 200 Hz cosine signal, with a 95% confidence interval.
Designing Digital FIR and IIR Filters
Signal Processing Toolbox enables you to design, analyze, and implement FIR and IIR digital filters in MATLAB.
Filter Responses and Design Methods
The toolbox supports a wide range of response types and design methods, including:
• Filter responses for lowpass, highpass, bandpass, bandstop, Hilbert, differentiator, pulse-shaping, and arbitrary magnitude filters
• Parks-McClellan and Kaiser window for FIR filter design
• Butterworth, Chebyshev Type I and Type II, and elliptic filters for IIR filter design
MATLAB code and corresponding plots for FIR (top right) and IIR (bottom right) filter design using algorithms in Signal Processing Toolbox.
Analyzing Filters
You can analyze your filter design by simultaneously viewing multiple characteristics in the Filter Visualization Tool (FVTool):
• Magnitude response, phase response, and group delay in the frequency domain
• Impulse response and step response in the time domain
• Pole-zero information
FVTool also helps you evaluate filter performance by providing information about filter order, stability, and phase linearity. Once you design your filter, you can implement it using FIR and IIR filter structures.
Analysis of a lowpass FIR filter designed using a Kaiser window method. Example plots from Signal Processing Toolbox include (clockwise from top left): Magnitude and phase responses, impulse response, pole-zero plot, and filter order and stability information.
Interactive Filter Design and Analysis
Signal Processing Toolbox provides FDATool, FVTool, and Filterbuilder for interactive filter design and analysis. Together, these tools enable you to:
• Explore FIR and IIR design methods for a given filter specification
• Analyze filters by viewing filter characteristics, including magnitude response, phase response, group delay, pole-zero plot, impulse response, and step response
• Obtain filter information, such as filter order, stability, and phase linearity
• Import previously designed filters and filter coefficients stored in the MATLAB workspace and export filter coefficients
Filter Design and Analysis Tool (FDATool) showing magnitude response, filter order, and stability information for a lowpass FIR filter.
Designing Analog Filters
Signal Processing Toolbox provides functions for analog filter design and analysis. Supported analog filter types include Butterworth, Chebyshev, Bessel, and elliptic. The toolbox also contains discretization functions for analog-to-digital filter conversion.
http://www.mathworks.com/products/signal/description5.html
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