Fast method to compute large Covariance Matrix on FPGA?

Hi, i have a serious problem with the architecture of a correlator for a
planar antenna array (16 x 16).
Theoretically i can't implent the normal expression sum(X*X^H) because i
would obtain a covariance matrix of 256 x 256. Then i can think to
implement the spatial smoothing technique, namely it takes an average of
overlapped subarray, with the advantage to have a smaller covariance
matrix. This is right but is slow technique!! I need efficient and fast
method to compute the covariance matrix on FPGA. with a less number of
multiplier possible. Infact for a covariance matrix 16 x 16 i need about
6000 multipliers! So i have seen the correlators based on hard-limiting (
sign+xor + counter) at this link

https://www.google.it/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&ved=0CDYQFjACahUKEwjwsKjT257IAhVlgXIKHQKFCWw&url=http%3A%2F%2Fhandle.dtic.mil%2F100.2%2FADA337434&usg=AFQjCNG5QUylZORV9KFHYizyu1QJZSBM5A&bvm=bv.103627116,d.d2s&cad=rja

but i don't know if this technique is right, on simulink is very
different
from the results of normal correlator.
Can someone help me?

thanks
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