Covariance Matching Estimation Techniques for Array Signal Processing Applications

y(t) = Ax(t) + e(t) (1) where y(t) ∈ Cm×1 and A = A(θ) ∈ Cm×nθ . It is assumed that: • Emitter signals x(t) are random. • Observation vectors {y(t)}t=1,2,... are i.i.d. circular Gaussian random variables with zero mean (, i.e. eiφZ has the same probability distribution as Z for all real φ, see for example [3]). • The emitter signal x(t) and the noise e(t) are uncorrelated which gives that R(θ, μ, σ) = E [y(t)y∗(t)] = Rs(θ, μ) } {{ } =E[Ax(t)x∗(t)A∗] + Q(σ) } {{ } =E[e(t)e∗(t)] . (2)