Hi,
I need to use sliding window algorithm,but it's the first time that I face to use it , so I need help to implement the following in matlab :
I have a radar_noise vector x with size (5000*1),how can I find covariance matrix by using sliding window algorithm?
Also I have a radar_received signal vector s with size (5000*1),how can I use sliding window to find the received signal model ,providing that :
The number of Quantization =2.
The number of samples = 32.
The signal model used is as follows: Consider a radar system utilizing an Nselement array with interelement spacing d.
The radar transmits an Mtpulse waveform in its coherent processing interval (CPI). The received data can then be partitioned in both space and time, by using a sliding window,into an (N*M) spacetime snapshot X'.
This partitioning will result in K = (Ns N +1)(Mt M +1) snapshot matrices being generated for processing.
The columns of these spacetime snapshots are then stacked into interleaved column vectors xk of size (NM*1).
The K columns are then arranged as the columns of the (NM*K )matrix X. The signal model used is then: X =ast' N where both s and t are spacetime vectors and a is a complex amplitude.
N is the (NM * K ) zeromean Gaussian clutterplusnoise matrix with independent and identically distributed (iid) columns nk approximately CN (0,C),where CN is complex Gaussian noise and C is the covariance matrix.
The spacetime clutterplusnoise covariance matrix is defined as C,where E[N * Hermitian(N)] and E[.] is the expectation operator.
Thanks
