This doctoral dissertation proposes adaptive signal processing schemes for detection and estimation with phased array radars. In radar system, target detection and estimation is ultimately obtaining adaptive weights which suppress unwanted signal and enlarge target signal.
In the first part of the dissertation, we focused on the target detection point of view with interference suppression, while the second part emphasis on the target information estimation. Lastly, the third part deals with the scheme that detects target and estimates target parameter simultaneously. For an airborne radar, clutter signal is the most severe interference which should be canceled for a target detection. Much of the recent researches in space-time adaptive processing (STAP) have been driven for a slow moving target. In this dissertation, the need to deal with non-homogeneous clutter is emphasized. This dissertation presents an
extension of the low-complexity, sigma-delta ($\Sigma\Delta$) algorithm incorporating the direct data domain ($D^3$) processing as a domain transformer. The new algorithm is practical and improves target detection in non-homogeneous clutter environments. The algorithm employs a hybrid approach, combining $D^3$ processing with the more traditional statistical approach, thereby obtaining advantages of both. In this dissertation, first, a modified $D^3$ algorithm, which maximizes signal-to-interference-plus-noise ratio(SINR), is presented. Then this $D^3$ algorithm is used as an adaptive transformer to create sum ($\Sigma$) and difference ($\Delta$) beams. The residual interference after the $D^3$ processing is further canceled by $\Sigma\Delta$ STAP. The proposed hybrid algorithm using $D^3-\Sigma\Delta$ STAP is tested in non-homogeneous clutter modeled using spherically invariant random variables(SIRV) and artificially injected discrete interferers. Performance of the proposed methods is compared with those of traditional statistical approaches, illustrating ...