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We have these two models :

z = h(x) + e, r1 = z - z_hat = h(x) - h(x_hat)

z = Bx + e, r2 = z - z_hat = Bx - Bx_hat

in the first equation x is estimated by nonlinear weighted least square and second one is estimated by linear weighted least square.

1) How the covariance matrix of r1 and r2 are calculated?('e' follows a Gaussian distribution with zero mean and covariance matrix R).

2)In Linear weighted least square, if B and x are complex matrix and vector respectively, what happens for residual covariance matrix?

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  • $\begingroup$ I forgot to write R is a diagonal matrix $\endgroup$ – neda Jan 24 at 6:44

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