Dimensionality reduction by signal-dependent adaptive linear transformation applied to STAP radars

Abstract

One of the key applications of space-time adaptive processing (STAP) is the detection of targets by surveillance radar systems, most notably by airborne radars and possibly in presence of strong interference signals. Notwithstanding, the ever growing number of elements used to build phased array antennas yields an amount of processing data that prevents practical implementation of full-rank processing and imposes a limit to the applicability of reduced-rank techniques as far as hardware technology and real-time systems requirements are concerned. This work proposes the application of an adaptive and signal dependent reduced-rank linear transformation (RLT) method to radar systems space-time processing. One will verify the computational complexity reduction resulting from the application of the method, as well as its performance in terms of STAP metrics, will be compared with other established reduced-rank techniques available in the literature. In order to verify its performance, the application of the RLT method to STAP radar systems will be employed on a scenario with a fixed platform, in presence of strong clutter and jamming scenario.