A novel CFAR detector in heterogeneous environment

In nonuniform situations, training data may not be representative of the disturbance in the Cell under Test (CUT) and the CA-CFAR exhibits strong degradations both in the detection performance as well as in the CFAR behaviour. Therefore a GIS-based training data selector and a statistical clutter edge selector are used to obtain homogeneous clutter field before the CFAR process. The simulation result in the presence of real data shows that the proposed CFAR detector not only keeps lower number of false alarms, but also improves the detection performance of classical CFAR detector by taking use of the environmental context and the clutter field parameters