Multichannel parametric detectors for airborne radar applications

Abstract

We consider the problem of detecting a multichannel signal in the presence of spatially and temporally colored disturbances. The parametric Rao and GLRT detectors, recently developed by exploiting a multichannel autoregressive (AR) model for the disturbance, have been shown to perform well with limited or even no range training data. The performance of the parametric detectors, however, has been evaluated through the limited computer simulations. The disturbances were generated to follow the exact multichannel AR processes and independently from each other with the same distribution whereas the disturbances in an airborne radar environment do not follow the exact multichannel AR model. In this paper, we evaluate the detection performance of the parametric Rao and GLRT detectors using airborne data obtained from the multi-channel airborne radar measurement (MCARM) database. This data contain typical clutter found in airborne radar systems, and cover a variety of scenarios including dense-target or heterogeneous environment Numerical results show that the parametric Rao and GLRT detectors work well with limited or even no range training data in an airborne radar environment.