The Stochastic Crb For Array | Processing A Textbook Derivation

where ( \mu, \nu ) denote any real-valued scalar parameter in ( \boldsymbol\Theta ). This formula is a workhorse in array processing derivations. It stems from the general result for zero-mean complex Gaussian vectors:

We derive the FIM for the stochastic model. Let ( \mathbfY = [\mathbfy(1), \dots, \mathbfy(N)] ) be the ( M \times N ) data matrix. The log-likelihood function (ignoring constants) is: where ( \mu, \nu ) denote any real-valued