What is the distinction between ergodic and stationary? An ergodic process is one where its statistical properties, like variance, can be deduced from a sufficiently long sample E g , the sample mean converges to the true mean of the signal, if you average long enough Now, it seems to me that a signal would have to be stationary, in order to be ergodic
Autocorrelation function and correlation integral But this is more a definition hypothesis than something that could be mathematically derived in general (as far as I know) Related answers: What is the distinction between ergodic and stationary? What is a good example of an ergodic process? What's the meaning of ergodicity?
What is a covariance matrix? - Signal Processing Stack Exchange Suppose you have k samples from each of the N elements of a uniform linear array (ULA) of sensors: What is the physical meaning of a covariance matrix? How do you form a covariance matrix with the
Autocorrelation-properties - Signal Processing Stack Exchange How do we go about proving the property that entails; If X(t) is ergodic with no periodic components the autocorrelation converges to square of the mean as the time difference(τ) approaches infinit
Sum of Sine and Cosine with Random Phase as LTI System The processes $ {Y}_ {1} $ and $ {Y}_ {2} $ aren't Gaussian (And not Ergodic) Clearly over time (The Phase is constant) the process $ {Y}_ {i} $ is Gaussian Yet the ensemble isn't Gaussian since each of its realization is retrieved from Gaussian Distribution with different parameters The process $ r (t) $ is indeed Gaussian