On the use of space-time adaptive processing and time-frequency data representations for detection of near-stationary targets in monostatic clutter

The detection of near-stationary targets in mainlobe clutter is a problem that has recently generated a great deal of interest within the Department of Defense community. Some examples of these types of targets are surface vehicles, missile launchers and loitering (micro-) unmanned aerial vehicles (UAV). The root of the difficulty lies in the fact that conventional radar processing loses the ability to use the Doppler of the target to discriminate it from the clutter. Indeed, the target need not even be nearly stationary for this to be a problem - even a rapidly moving target can exhibit low Doppler if its velocity vector is nearly perpendicular to the velocity vector of the observation platform. Raytheon Systems Company (Raytheon) has been investigating a number of advanced algorithmic solutions to this problem within the context of providing a dual-mission capability to currently fielded RF missile systems. This paper describes a processing architecture that combines preprocessing, time-frequency transforms and best bases algorithms and discusses some preliminary results