Matrix and autocorrelation

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.

On Mar 12, 11:02&#4294967295;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.

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