Background and Clutter Removal Algorithm for RCS Extraction in Semi-Anechoic Chamber

The purpose of radar cross-section (RCS) measurement is to determine the amount of scattering that occurs when the radar signal illuminates the target. It is generally performed to prove a design concept. RCS measurement chamber requires a good signal-to-noise ratio during the measurement. When the measurement is performed in a non-controlled environment, coherent background subtraction associated with time gating is commonly used to improve the quality of the RCS data. Although these techniques are usually effective, residual clutter and background level still need to be removed to accurately characterize the target's RCS in highly cluttered environments, such as semi-anechoic chambers. In this paper, a four-step post-processing technique is presented. In addition to the vector background subtraction and time-gating techniques implemented in our previous work, a statistical algorithm called Principal Component Analysis (PCA) is applied to the ISAR image of the target. It is an extension of the PCA technique to RCS measurement. It is shown that residual background and clutter can be reduced by the statistical filtering method through eigenvalue decomposition of the RCS data. The technique is presented and evaluated through measurement of the RCS of a dihedral corner reflector at the X-band in the semi-anechoic chamber of the Directed Energy Research Center.