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Thread Subject:
sliding window

Subject: sliding window

From: zayed

Date: 28 Dec, 2011 23:51:08

Message: 1 of 1


    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 Ns-element array with inter-element spacing d.

The radar transmits an Mt-pulse 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) space-time snapshot X'.

This partitioning will result in K = (Ns -N +1)(Mt -M +1) snapshot matrices being generated for processing.

The columns of these space-time snapshots are then stacked into inter-leaved 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 space-time vectors and a is a complex amplitude.

N is the (NM * K ) zero-mean Gaussian clutter-plus-noise 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 space-time clutter-plus-noise covariance matrix is defined as C,where E[N * Hermitian(N)] and E[.] is the expectation operator.


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