Spectral Estimation
Analyze the spectral content of uniformly or nonuniformly sampled signals using
        periodogram, pwelch, or 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. 
Apps
| Signal Analyzer | Visualize and compare multiple signals and spectra | 
Functions
Topics
- Nonparametric MethodsLearn about the periodogram, modified periodogram, Welch, and multitaper methods of nonparametric spectral estimation. 
- Detect a Distorted Signal in NoiseUse frequency analysis to characterize a signal embedded in noise. 
- Measure the Power of a SignalEstimate 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 PaddingObtain an accurate estimate of the amplitude of a sinusoidal signal using zero padding. 
- Bias and Variability in the PeriodogramReduce bias and variability in the periodogram using windows and averaging. 
- Compare the Frequency Content of Two SignalsIdentify similarity between signals in the frequency domain. 
- Find Periodicity Using Frequency AnalysisSpectral analysis helps characterize oscillatory behavior in data and measure the different cycles. 
- Significance Testing for Periodic ComponentAssess the significance of a sinusoidal component in white noise using Fisher's g-statistic. 
- Cross Spectrum and Magnitude-Squared CoherenceObtain 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. 




