Moving target detection with limited training data based on the subspace orthogonal projection

Abstract

The number of training data is usually limited for moving target detection in airborne radar, which can significantly degrade the performance of detectors. In this study, the authors propose a detector for detecting moving targets based on the random matrix theory (RMT). The clutter subspace is first estimated through the RMT. Then the data under test are projected onto the orthogonal complement space of the clutter subspace for whitening. Finally, the generalised energy accumulation detection of the whitened data is carried out. Simulation results show that the proposed detector can detect moving targets effectively even when the number of training data is extremely small and the detector has a fast rate of convergence and constant false alarm rate.