| STAP (Space-Time Adaptive Processing) radar is a future generation radar, which improves low-velocity target detection by means of better mainlobe clutter suppression. STAP permits the detection of targets with a smaller cross-section that might otherwise be obscured by side-lobe clutter. Also STAP provides a capability to handle nonstationary interference. In this dissertation, four CFAR (Constant-False-Alarm-Rate) detection algorithms are developed for STAP airborne radar systems.; Algorithm 1 provides a CFAR detection and a ML (Maximum Likelihood) estimation of the steering vector of the target over all angles and Dopplers. This conventional CFAR detection algorithm uses all of the degrees of freedom for space-time processing, but in so doing involves a time-consuming matrix-inversion operation. Algorithm 2 is a reduced-rank CFAR detection test which, in principle, involves no matrix inversion operations.; Although Algorithm 2 provides a fast CFAR detection test, there still is a need for a large amount of range-gate data to achieve a given detection performance. Algorithm 3 proposes a reduced-rank and sample CFAR detection test in which a much smaller amount of sample data is needed compared to the requirement of the test in Algorithm 2.; Finally, a sum of Q frequency-hopped STAP radar returns is suggested as a non-coherent integrating CFAR detection algorithm of Q reduced-rank and sample tests. This new non-coherent detection criterion provides a significant improvement in the detection performance without a need to increase the transmitter power. |
