RCS-Based Imaging of Extended Targets for Classification in Multistatic Radar Systems

Efficient non-co-operative target imaging and classification are crucial for defense radar systems. Radar Cross Section (RCS) images provide distinctive characteristics of targets. They are easily measurable and hence can be used as features for accurate target classification. In this work, a low-complexity composite RCS imaging technique of the detected extended targets is developed using the inverse synthetic aperture radar oriented approach in a distributed multistatic radar system. The algorithm employs what we call a “floating grid-based formulation” which helps to overcome the exact time and phase alignment shortcomings in the fusion of measurements. The RCS values in the grid considered are estimated using a robust recovery technique. Bistatic radar cross-section values obtained for different transmitter-receiver pairs are fused to obtain a comprehensive RCS image of the target. This image is also utilized to derive the synthetic shape of the target which also gives a notion of the dimension of the target. Simulation results show that the multi static radar cross-section images of different extended target shapes obtained are different. The synthetic shapes derived for the targets are also distinct. This way of imaging the RCS and shape provides a unique representation of the target signatures thus, can be used as potential features for good target classification.