A Study on Low RCS Target Classification Based on PCL Radar Network Using Statistical Hypothesis Test

MasterThis paper proposes a new target classification algorithm based on bistatic radar cross section (RCS) in passive coherent location (PCL) radar network. First, we define the expected target scenario to construct the training set consisting bistatic RCS distribution of each target from PCL radar network which has multiple transmitters and a single receiver. Next, by measuring a test set consists of bistatic RCSs, we classify each target by utilizing the statistical hypothesis test algorithms such as Z-test, Wilcoxon-test and Sign-test. It has been found that the proposed framework exhibits good performance in terms of accuracy and computational efficiency, compared to the conventional method using time-varying RCS from a monostatic radar