Analyze the spectral content of uniformly or nonuniformly sampled signals using
plomb. Sharpen periodogram estimates using reassignment. Determine frequency-domain coherence between signals. Estimate transfer functions based on input and output measurements. Study MIMO systems in the frequency domain.
|Signal Analyzer||Visualize and compare multiple signals and spectra|
|Cross power spectral density|
|Find local maxima|
|Spectral entropy of signal|
|Periodogram power spectral density estimate|
|Multitaper power spectral density estimate|
|Generate octave spectrum|
|Analyze signals in the frequency and time-frequency domains|
|Welch’s power spectral density estimate|
|Transfer function estimate|
- Nonparametric Methods
Learn about the periodogram, modified periodogram, Welch, and multitaper methods of nonparametric spectral estimation.
- Detect a Distorted Signal in Noise
Use frequency analysis to characterize a signal embedded in noise.
- Measure the Power of a Signal
Estimate the width of the frequency band that contains most of the power of a signal. For distorted signals, determine the power stored in the fundamental and the harmonics.
- Amplitude Estimation and Zero Padding
Obtain an accurate estimate of the amplitude of a sinusoidal signal using zero padding.
- Bias and Variability in the Periodogram
Reduce bias and variability in the periodogram using windows and averaging.
- Compare the Frequency Content of Two Signals
Identify similarity between signals in the frequency domain.
- Significance Testing for Periodic Component
Assess the significance of a sinusoidal component in white noise using Fisher's g-statistic.
- Cross Spectrum and Magnitude-Squared Coherence
Obtain the phase lag between sinusoidal components and identify frequency-domain correlation in a time series.
- Price Weather Derivatives (Financial Instruments Toolbox)
This example demonstrates a workflow for pricing weather derivatives based on historically observed temperature data.