The auto-correlation function Rx(τ) satisfies which one of the fol...
T) is a mathematical function that describes how a signal or time series is correlated with itself at different time lags. It is defined as the expected value of the product of two time-shifted samples of a signal or time series, given by:
Rx(t) = E[X(t) X(t + τ)]
where X(t) is the signal or time series at time t, τ is the time lag, and E[·] denotes the expected value operator.
The auto-correlation function is a useful tool for analyzing the properties of time series data, such as their periodicity, stationarity, and spectral content. It can be used to estimate the power spectrum of a signal, which describes the distribution of its energy over different frequencies.
The auto-correlation function is related to the Fourier transform of the signal through the Wiener-Khinchin theorem, which states that the power spectral density of a stationary signal is the Fourier transform of its auto-correlation function.
In practice, the auto-correlation function is often estimated from a finite sample of data using statistical methods such as the sample auto-correlation function or the maximum likelihood estimator. These estimators are commonly used in time series analysis, signal processing, and statistical inference.
The auto-correlation function Rx(τ) satisfies which one of the fol...
The autocorrelation function of a signal is defined as the measure of similarity or coherence between a signal and its time delayed version. Thus, autocorrelation is the correlation of a signal with itself.
Property of autocorrelation function:
- The autocorrelation function of energy signals exhibits complex conjugate symmetry, which means the real part of autocorrelation function R(τ) is an even function of delay parameter ( τ) and the imaginary part of R(τ) is an odd function of the parameter τ. Thus,
R(τ)=R∗(−τ)