Development of Novel Knowledge-Based Space-Time Adaptive Processing (STAP) Techniques

Airborne radar systems face a number of difficulties in detection and estimation of targets in the presence of clutter and other forms of interference. The work proposed for this STTR will focus on advancement and application of robust, innovative methods that improve Space-Time Adaptive Processing (STAP) for airborne radar. As the name implies STAP allows a system to adaptively respond to a changing environment in both the spatial and temporal domains. STAP improves the detection and estimation process by placing nulls in the adapted array pattern along directions in space (angle) and in time (Doppler) occupied by interference. This allows for detection of targets at the output of the STAP processor that, in principle, need only compete with thermal noise. Many of the STAP techniques proposed in the past or in use today, rely on the validity of certain assumptions regarding the interference environment, the operating environment, the characteristics of the radar array and the characteristics of the radar transmit/receive channels. Acceptable performance, for many techniques, can be achieved only by assuming certain unrealistic conditions. These unrealistic assumptions include independent, identically distributed (i.i.d. or non-stationary) clutter statistics, large quantities of i.i.d. training data, direct-path-only jamming, identical element patterns, phase, amplitude and frequency matched channels, no steering vector mismatch, no near-field distortion of the array response from aircraft induced multipath, no internal clutter motion and no target contamination of training data.