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periodogram

Periodogram power spectral density estimate

Syntax

  • [pxx,w] = periodogram(___)
  • [pxx,f] = periodogram(___,fs) example
  • [pxx,w] = periodogram(x,window,w) example
  • [pxx,f] = periodogram(x,window,f,fs) example
  • [___] = periodogram(x,window,___,freqrange) example
  • [___] = periodogram(x,window,___,spectrumtype) example
  • [pxx,f,pxxc] = periodogram(___,'ConfidenceLevel',probability) example
  • periodogram(___)

Description

example

pxx = periodogram(x) returns the periodogram power spectral density (PSD) estimate of the input signal, x, using a rectangular window. If x is real-valued, pxx is a one-sided PSD estimate. If x is complex-valued, pxx is a two-sided PSD estimate. The number of points, nfft, in the discrete Fourier transform (DFT) is the maximum of 256 or the next power of two greater than the signal length.

example

pxx = periodogram(x,window) returns the modified periodogram PSD estimate using the window, window. window is a vector the same length as x.

example

pxx = periodogram(x,window,nfft) uses nfft points in the discrete Fourier transform (DFT). If nfft is greater than the signal length, x is zero-padded to length nfft. If nfft is less than the signal length, the signal is wrapped modulo nfft and summed using datawrap. For example, the input signal [1 2 3 4 5 6 7 8] with nfft equal to 4 results in the periodogram of sum([1 5; 2 6; 3 7; 4 8],2).

[pxx,w] = periodogram(___) returns the normalized frequency vector, w. If pxx is a one-sided periodogram, w spans the interval [0,π] if nfft is even and [0,π) if nfft is odd. If pxx is a two-sided periodogram, w spans the interval [0,2π).

example

[pxx,f] = periodogram(___,fs) returns a frequency vector, f, in cycles per unit time. The sampling frequency, fs, is the number of samples per unit time. If the unit of time is seconds, then f is in cycles/sec (Hz). For real–valued signals, f spans the interval [0,fs/2] when nfft is even and [0,fs/2) when nfft is odd. For complex-valued signals, f spans the interval [0,fs).

example

[pxx,w] = periodogram(x,window,w) returns the two-sided periodogram estimates at the normalized frequencies specified in the vector, w. The vector, w, must contain at least 2 elements.

example

[pxx,f] = periodogram(x,window,f,fs) returns the two-sided periodogram estimates at the frequencies specified in the vector, f. The vector, f, must contain at least 2 elements. The frequencies in f are in cycles per unit time. The sampling frequency, fs, is the number of samples per unit time. If the unit of time is seconds, then f is in cycles/sec (Hz).

example

[___] = periodogram(x,window,___,freqrange) returns the periodogram over the frequency range specified by freqrange. Valid options for freqrange are: 'onesided', 'twosided', or 'centered'.

example

[___] = periodogram(x,window,___,spectrumtype) returns the PSD estimate if spectrumtype is specified as 'psd' and returns the power spectrum if spectrumtype is specified as 'power'.

example

[pxx,f,pxxc] = periodogram(___,'ConfidenceLevel',probability) returns the probabilityx100% confidence intervals for the PSD estimate in pxxc.

periodogram(___) with no output arguments plots the periodogram PSD estimate in dB per unit frequency in the current figure window.

Examples

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Periodogram Using Default Inputs

Obtain the periodogram of an input signal consisting of a discrete-time sinusoid with an angular frequency of π/4 radians/sample with additive N(0,1) white noise.

Create a sine wave with an angular frequency of π/4 radians/sample with additive N(0,1) white noise. The signal is 320 samples in length. Obtain the periodogram using the default rectangular window and DFT length. The DFT length is the next power of two greater than the signal length, or 512 points. Because the signal is real-valued and has even length, the periodogram is one-sided and there are 512/2+1 points.

n = 0:319;
x = cos(pi/4*n)+randn(size(n));
pxx = periodogram(x);
plot(10*log10(pxx))

Modified Periodogram with Hamming Window

Obtain the modified periodogram of an input signal consisting of a discrete-time sinusoid with an angular frequency of π/4 radians/sample with additive N(0,1) white noise.

Create a sine wave with an angular frequency of π/4 radians/sample with additive N(0,1) white noise. The signal is 320 samples in length. Obtain the modified periodogram using a Hamming window and default DFT length. The DFT length is the next power of two greater than the signal length, or 512 points. Because the signal is real-valued and has even length, the periodogram is one-sided and there are 512/2+1 points.

n = 0:319;
x = cos(pi/4*n)+randn(size(n));
pxx = periodogram(x,hamming(length(x)));
plot(10*log10(pxx))

DFT Length Equal to Signal Length

Obtain the periodogram of an input signal consisting of a discrete-time sinusoid with an angular frequency of π/4 radians/sample with additive N(0,1) white noise. Use a DFT length equal to the signal length.

Create a sine wave with an angular frequency of π/4 radians/sample with additive N(0,1) white noise. The signal is 320 samples in length. Obtain the periodogram using the default rectangular window and DFT length equal to the signal length. Because the signal is real-valued, the one-sided periodogram is returned by default with a length equal to 320/2+1.

n = 0:319;
x = cos(pi/4*n)+randn(size(n));
nfft = length(x);
pxx = periodogram(x,[],nfft);
plot(10*log10(pxx))

Periodogram of Relative Sunspot Numbers

Obtain the periodogram of the Wolf (relative sunspot) number data sampled yearly between 1700 and 1987.

Load the relative sunspot number data. Obtain the periodogram using the default rectangular window and number of DFT points (512 in this example). The sampling rate for these data is 1 sample/year. Plot the periodogram.

load sunspot.dat
relNums=sunspot(:,2);
[pxx,f] = periodogram(relNums,[],[],1);
plot(f,10*log10(pxx))
xlabel('Cycles/Year'); ylabel('dB');
title('Periodogram of Relative Sunspot Number Data');

You see in the preceding figure that there is a peak in the periodogram at approximately 0.1 cycles/year, which indicates a period of approximately 10 years.

Periodogram Using Goertzel's Algorithm — Normalized Frequency

Obtain the periodogram of an input signal consisting of two discrete-time sinusoids with an angular frequencies of π/4 and π/2 radians/sample in additive N(0,1) white noise. Use Goertzel's algorithm to obtain the two-sided periodogram estimates at π/4 and π/2 radians/sample. Compare the result to the one-sided periodogram.

n = 0:319;
x = cos(pi/4*n)+0.5*sin(pi/2*n)+randn(size(n));
[pxx,w] = periodogram(x,[],[pi/4 pi/2]);
pxx
[pxx1,w1] = periodogram(x);
plot(w1,pxx1)

You see that the periodogram values obtained using Goertzel's algorithm are 1/2 the values in the one-sided periodogram. This is consistent with the fact that using Goertzel's algorithm returns the two-sided periodogram.

Periodogram Using Goertzel's Algorithm — Frequency in Hz

Create a signal consisting of two sine waves with frequencies of 100 and 200 Hz in N(0,1) white additive noise. The sampling frequency is 1 kHz. Use Goertzel's algorithm to obtain the two-sided periodogram at 100 and 200 Hz.

fs = 1000;
t = 0:0.001:1-0.001;
x = cos(2*pi*100*t)+sin(2*pi*200*t)+randn(size(t));
freq = [100 200];
[pxx,f] = periodogram(x,[],freq,fs);

Upper and Lower 95%-Confidence Bounds

The following example illustrates the use of confidence bounds with the periodogram. While not a necessary condition for statistical significance, frequencies in the periodogram where the lower confidence bound exceeds the upper confidence bound for surrounding PSD estimates clearly indicate significant oscillations in the time series.

Create a signal consisting of the superposition of 100-Hz and 150-Hz sine waves in additive white N(0,1) noise. The amplitude of the two sine waves is 1. The sampling frequency is 1 kHz.

t = 0:0.001:1-0.001;
fs = 1000;
x = cos(2*pi*100*t)+sin(2*pi*150*t)+randn(size(t));

Obtain the periodogram with 95%-confidence bounds. Plot the periodogram along with the confidence interval and zoom in on the frequency region of interest near 100 and 150 Hz.

[pxx,f,pxxc] = periodogram(x,rectwin(length(x)),length(x),fs,...
'ConfidenceLevel', 0.95);
plot(f,10*log10(pxx)); hold on;
plot(f,10*log10(pxxc),'r--','linewidth',2);
axis([85 175 min(min(10*log10(pxxc))) max(max(10*log10(pxxc)))]);
xlabel('Hz'); ylabel('dB');
title('Periodogram with 95%-Confidence Bounds');

At 100 and 150 Hz, the lower confidence bound exceeds the upper confidence bounds for surrounding PSD estimates.

Power Estimate of Sinusoid

Estimate the power of sinusoid at a specific frequency using the 'power' option.

Create a 100-Hz sinusoid one second in duration sampled at 1 kHz. The amplitude of the sine wave is 1.8, which equates to a power of 1.82/2 = 1.62. Estimate the power using the 'power' option.

fs = 1000;
t = 0:1/fs:1-1/fs;
x = 1.8*cos(2*pi*100*t);
[pxx,f] = periodogram(x,hamming(length(x)),length(x),fs,'power');
[pwrest,idx] = max(pxx);
fprintf('The maximum power occurs at %3.1f Hz\n',f(idx));
fprintf('The power estimate is %2.2f\n',pwrest);

DC-Centered Periodogram

Obtain the periodogram of a 100-Hz sine wave in additive N(0,1) noise. The data are sampled at 1 kHz. Use the 'centered' option to obtain the DC-centered periodogram and plot the result.

fs = 1000;
t = 0:0.001:1-0.001;
x = cos(2*pi*100*t)+randn(size(t));
[pxx,f] = periodogram(x,[],length(x),fs,'centered');
plot(f,10*log10(pxx))
xlabel('Hz'); ylabel('dB')

Input Arguments

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x — Input signalvector

Input signal, specified as a row or column vector.

Data Types: single | double
Complex Number Support: Yes

window — Windowrectwin(length(x)) (default) | [] | vector

Window, specified as a row or column vector the same length as the input signal. If you specify window as empty, the default rectangular window is used.

Data Types: single | double

nfft — Number of DFT pointsmax(256,2^nextpow2(length(x)) (default) | integer | []

Number of DFT points, specified as a positive integer. For a real-valued input signal, x, the PSD estimate, pxx has length (nfft/2+1) if nfft is even, and (nfft+1)/2 if nfft is odd. For a complex-valued input signal,x, the PSD estimate always has length nfft. If nfft is specified as empty, the default nfft is used.

Data Types: single | double

fs — Sampling frequencypositive scalar

Sampling frequency specified as a positive scalar. The sampling frequency is the number of samples per unit time. If the unit of time is seconds, the sampling frequency has units of hertz.

w — Normalized frequencies for Goertzel algorithmvector

Normalized frequencies for Goertzel algorithm, specified as a row or column vector with at least 2 elements. Normalized frequencies are in radians/sample.

Example: w = [pi/4 pi/2]

Data Types: double

f — Cyclical frequencies for Goertzel algorithmvector

Cyclical frequencies for Goertzel algorithm, specified as a row or column vector with at least 2 elements. The frequencies are in cycles per unit time. The unit time is specified by the sampling frequency, fs. If fs has units of samples/second, then f has units of Hz.

Example: fs = 1000; f= [100 200]

Data Types: double

freqrange — Frequency range for PSD estimate'onesided' | 'twosided' | 'centered'

Frequency range for the PSD estimate, specified as a one of 'onesided', 'twosided', or 'centered'. The default is 'onesided' for real-valued signals and 'twosided' for complex-valued signals. The frequency ranges corresponding to each option are

  • 'onesided' — returns the one-sided PSD estimate of a real-valued input signal, x. If nfft is even, pxx will have length nfft/2+1 and is computed over the interval [0,π] radians/sample. If nfft is odd, the length of pxx is (nfft+1)/2 and the interval is [0,π) radians/sample. When fs is optionally specified, the corresponding intervals are [0,fs/2] cycles/unit time and [0,fs/2) cycles/unit time for even and odd length nfft respectively.

  • 'twosided' — returns the two-sided PSD estimate for either the real-valued or complex-valued input, x. In this case, pxx has length nfft and is computed over the interval [0,2π) radians/sample. When fs is optionally specified, the interval is [0,fs) cycles/unit time.

  • 'centered' — returns the centered two-sided PSD estimate for either the real-valued or complex-valued input, x. In this case, pxx has length nfft and is computed over the interval (-π,π] radians/sample for even length nfft and (-π,π) radians/sample for odd length nfft. When fs is optionally specified, the corresponding intervals are (-fs/2, fs/2] cycles/unit time and (-fs/2, fs/2) cycles/unit time for even and odd length nfft respectively.

Data Types: char

spectrumtype — Power spectrum scaling'psd' (default) | 'power'

Power spectrum scaling, specified as one of 'psd' or 'power'. Omitting the spectrumtype, or specifying 'psd', returns the power spectral density. Specifying 'power' scales each estimate of the PSD by the equivalent noise bandwidth of the window. Use the 'power' option to obtain an estimate of the power at each frequency.

Data Types: char

probability — Confidence interval for PSD estimate0.95 (default) | scalar in the range (0,1)

Coverage probability for the true PSD, specified as a scalar in the range (0,1). The output, pxxc, contains the lower and upper bounds of the probabilityx100% interval estimate for the true PSD.

Output Arguments

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pxx — PSD estimatevector

PSD estimate, specified as a real-valued, nonnegative column vector. The units of the PSD estimate are in squared magnitude units of the time series data per unit frequency. For example, if the input data is in volts, the PSD estimate is in units of squared volts per unit frequency. For a time series in volts, if you assume a resistance of 1 ohm and specify the sampling frequency in Hz, the PSD estimate is in watts/Hz.

Data Types: single | double

w — Normalized frequenciesvector

Normalized frequencies, specified as a real-valued column vector. If pxx is a one-sided PSD estimate, w spans the interval [0,π] if nfft is even and [0,π) if nfft is odd. If pxx is a two-sided PSD estimate, w spans the interval [0,2π). For a DC-centered PSD estimate, f spans the interval (-π,π] radians/sample for even length nfft and (-π,π) radians/sample for odd length nfft.

Data Types: double

f — Cyclical frequenciesvector

Cyclical frequencies, specified as a real-valued column vector. For a one-sided PSD estimate, f spans the interval [0,fs/2] when nfft is even and [0,fs/2) when nfft is odd. For a two-sided PSD estimate, f spans the interval [0,fs). For a DC-centered PSD estimate, f spans the interval (-fs/2, fs/2] cycles/unit time for even length nfft and (-fs/2, fs/2) cycles/unit time for odd length nfft .

Data Types: double

pxxc — Confidence boundsmatrix

Confidence bounds, specified as an N-by-2 matrix with real-valued elements. The row dimension of the matrix is equal to the length of the PSD estimate, pxx. The first column contains the lower confidence bound and the second column contains the upper confidence bound for the corresponding PSD estimates in the rows of pxx. The coverage probability of the confidence intervals is determined by the value of the probability input.

Data Types: single | double

More About

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Periodogram

The periodogram is a nonparametric estimate of the power spectral density (PSD) of a wide-sense stationary random process. The periodogram is the Fourier transform of the biased estimate of the autocorrelation sequence. For a signal, xn, sampled at fs samples per unit time, the periodogram is defined as

where Δt is the sampling interval. For a one-sided periodogram, the values at all frequencies except 0 and the Nyquist, 1/2Δt, are multiplied by 2 so that the total power is conserved.

If the frequencies are in radians/sample, the periodogram is defined as

The frequency range in the preceding equations has variations depending on the value of the freqrange argument. See the description of freqrange in Input Arguments.

The integral of the true PSD, P(f), over one period, 1/Δt for cyclical frequency and 2π for normalized frequency, is equal to the variance of the wide-sense stationary random process.

For normalized frequencies, replace the limits of integration appropriately.

Modified Periodogram

The modified periodogram multiplies the input time series by a window function. A suitable window function is nonnegative and decays to zero at the beginning and end points. Multiplying the time series by the window function tapers the data gradually on and off and helps to alleviate the leakage in the periodogram. See Bias and Variability in the Periodogram for an example.

If hn is a window function, the modified periodogram is defined by

where Δt is the sampling interval.

If the frequencies are in radians/sample, the modified periodogram is defined as

The frequency range in the preceding equations has variations depending on the value of the freqrange argument. See the description of freqrange in Input Arguments.

See Also

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