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For the problem of optimizing the SINR, take w, v def ≡ wH Rv; then SINR = |a|2 | w, R−1 v |2 / w, w . Thus the SINR is maximized if and only if for an arbitrary complex constant α. The maximum SINR is Like Eq. (95), Eq. (103) is called the adaptive ﬁlter equation, and the components of w are called adaptive weights. Maximum Likelihood Estimate of Target Amplitude The adaptive ﬁlter equation is also a consequence of the maximum likelihood estimate of the target amplitude. The log likelihood function (ignoring constants) from Eq.
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This is prohibitively expensive for real-time operations even for modestly sized radars that use tens of pulses and tens of elements. Estimating the covariance matrix introduces an SINR loss proportional to the ratio of the number of adaptive degrees of freedom to the sample support (see the next section). The large number of degrees of freedom found in full-dimension STAP requires a correspondingly large number of samples to achieve low estimation losses, and the sample support is typically limited.