| This research advances adaptive interference suppression techniques for airborne radar, addressing the problem of target detection within severe interference environments characterized by high ground clutter levels, noise jammer infiltration, and strong discrete interferers. Two-dimensional (2D) Space-Time Adaptive Processing (STAP) concepts are extended into three-dimensions (3D) by casting each major 2D STAP research area into a 3D framework. The work first develops an appropriate 3D data model with provisions for range ambiguous clutter returns. Adaptive 3D development begins with two factored approaches, 3D Factored Time-Space (3D-FTS) and Elevation-Joint Domain Localized (Elev-JDL). The 3D-FTS technique exhibits greater than 15 dB improvement (over 2D-FTS) in Relative Peak Sidelobe Level (RPSL) using data from the Multi-Channel Airborne Radar Measurement (MCARM) program. The 3D adaptive development continues with optimal techniques, i.e., joint domain methods. First, the 3D Matched Filter (3D-MF) is derived followed by a 3D Adaptive Matched Filter (3D-AMF) discussion focusing on well-established practical limitations consistent with the 2D case. Finally, a 3D-JDL method is introduced and demonstrates target detection improvement of approximately 10∼dB and 57 dB when compared to 2D-JDL and 2D-FTS, respectively, using an 8 x 8 non-uniform rectangular array and eight pulses. Proposed 3D Hybrid methods extend current state-of-the-art 2D hybrid methods. The initial 3D hybrid, a functional extension of the 2D technique, exhibits distinct performance advantages in heterogeneous clutter. The final 3D hybrid method is virtually impervious to discrete interference; an RPSL of −16.15 dB, versus 8.77 dB and 6.56 dB for the inverse 3D hybrid and 3D extension, respectively, was achieved for a given data realization. An average RPSL of −9.71 dB with standard deviation of 5.58 dB was achieved across 500 realizations. |
