| High-Frequency Surface Wave Radar(HFSWR) is a radar technology that offers numerous advantages for low-cost, medium accuracy surveillance of coastal waters beyond the exclusive economic zone. However, target detection and tracking is primarily limited by ionospheric interference. Ionospheric clutter is characterized by a high degree of nonhomogeneity and nonstationarity, which makes its suppression difficult using conventional processing techniques. Space-time adaptive processing techniques have enjoyed great success in airborne radar, but have not yet been investigated in the context of HFSWR. This thesis is primarily concerned with the evaluation of existing STAP techniques in the HFSWR scenario and the development of a new multistage adaptive processing approach, dubbed the Fast Fully Adaptive (FFA) scheme, which was developed with the particular constraints of the HFSWR interference environment in mind. To this end an analysis of measured ionospheric clutter is conducted, which allows for a characterization of the underlying statistical behavior of the HFSWR interference. Several popular low-complexity STAP algorithms are assessed for their performance in HFSWR. Focus is then shifted towards the development of the FFA approach. Three different spatio-temporal partitioning schemes are introduced. A thorough investigation of the performance of the FFA is conducted in both the airborne radar and HFSWR setups under a variety of sample-support scenarios. |
