Maximum likelihood angle and velocity estimation with space-time adaptive processing radar

Airborne surveillance radar performance can be improved with space-time adaptive processing (STAP) to cancel ground clutter and interference. This paper considers maximum likelihood (ML) angle and velocity estimation for airborne radar employing STAP. The ML estimator requires a. two-dimensional optimization. A computationally efficient quasi-Newton approach is proposed, whereby a positive definite approximate Hessian is formed using only the secondary data processed by three adaptive space-time filters. The algorithm is naturally initialized by the target detection location within a coarsely spaced angle-Doppler filter bank. Monte-Carlo simulations show that the new algorithm nearly achieves the Cramer-Rao bound and outperforms conventional one-dimensional estimators, which suffer from location-dependent biases when employed in STAP scenarios.