Please who help me with this problem. A signal source consists of a perfect square wave of unitamplitude with additive noice of variance v^2. The signal is sampled by a processor, which has a sampling rateexactly 4timesthe fundamental frequency of the square wave. The processoris used todefine the autocorrelation matrix(order 4) ofthe signal. Determine the value of this matrix and its inverse. If you can forward materials that I be able to get materials for the above the better.

# Matrix and autocorrelation

Started by ●March 12, 2009

Reply by ●March 12, 20092009-03-12

On Mar 12, 11:02�am, "jpchibole" <jpchib...@yahoo.com> wrote:> Please who help me with this problem. A signal source consists of a perfect > square wave of unitamplitude with additive noice of variance v^2. The > signal is sampled by a processor, which has a sampling rateexactly > 4timesthe fundamental frequency of the square wave. The processoris used > todefine the autocorrelation matrix(order 4) ofthe signal. Determine the > value of this matrix and its inverse. > If you can forward materials that I be able to get materials for the above > the better.I would think your textbook and classnotes would suffice.

Reply by ●March 12, 20092009-03-12

On Mar 13, 4:02 am, "jpchibole" <jpchib...@yahoo.com> wrote:> Please who help me with this problem. A signal source consists of a perfect > square wave of unitamplitude with additive noice of variance v^2. The > signal is sampled by a processor, which has a sampling rateexactly > 4timesthe fundamental frequency of the square wave. The processoris used > todefine the autocorrelation matrix(order 4) ofthe signal. Determine the > value of this matrix and its inverse. > If you can forward materials that I be able to get materials for the above > the better.Use Fourier series to expand teh square wave into the required number of harmonics. You can tell how many from the sample rate. Then you have sine waves only to deal with. A sine wave autocorrelation is in all the text books. Only the amplitudes and freqs will be different. The white noise will give a diagonal matrix equal to the variance of the additive white noise. V^2. Hence this will only effect the diagonal terms. Hardy