Analysis of the Small Sample Size Performance of Fast Fully Adaptve STAP Techniques for MTI Radar.

摘要

In ground surveillance from an airborne or space-based radar it is desirable to be able to detect small moving targets, such as tanks or wheeled vehicles, within severe ground clutter. For operational moving target indication (MTI) systems the clutter filter coefficients have to be updated frequently due to rapidly changing interference environment. This report examines the small sample size performance of different fast fully adaptive space-time processors (STAP) and compares it to the optimum-detector performance. These recently proposed techniques, named Matrix Transformation based Projection (MTP) and Lean Matrix Inversion (LMI), were originally developed to provide fast man-made jammer suppression in large surface phased array radars with many elements. For this application they have been proven to operate with near-optimum performance, yet with a computational expense drastically reduced from that of the optimum detector in most practical cases. The investigation herein focuses on the performance achieved when only a few data samples are available to adapt (update) the clutter filter coefficient. In this report, the techniques are described and a number of simulations carried out. The two applications, STAP and jammer suppression, are similar; both are required to suppress an interference which is characterized by a certain number of dominant eigenvalues of the sample space-time covariance matrix. Despite the similarities the performance between the two differs due to the different shapes of their eigenvalue distribution. The LMI is shown to give the best Signal-to-Noise-plus- Clutter Ratio (SNCR) for a given computational load.