How to implement sliding-window algorithm in matlab ?

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zayed 2011-12-29

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https://ww2.mathworks.cn/matlabcentral/answers/24923-how-to-implement-sliding-window-algorithm-in-matlab

编辑： Walter Roberson 2017-1-16

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.

[EDITED: Jan Simon]: The following is copied from "ADAPTIVE ARRAY DETECTION ALGORITHMS WITH STEERING VECTOR MISMATCH", LIM Chin Heng, Elias Aboutanios and Bernard Mulgrew, published in Circuits and Systems, 2006. APCCAS 2006. IEEE Asia Pacific Conference

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.

Thanks