Ground clutter suppression method based on FNN for dual-polarisation weather radar - Hai Li; Jiawei Ren; Jungong Han; Yi Fan (The Journal of Engineering, 2019)
Conventional clutter suppression methods in the field of precipitation detection generally use fixed parameters, resulting in poor clutter suppression in complex weather conditions and regional environments. Here, a ground clutter suppression method for dual-polarisation weather radar based on fuzzy neural network (FNN) is proposed. In the method, the FNN is trained with the ground clutter data acquired by the dual-polarisation weather radar in the clear sky mode. The membership function parameters of ground clutter polarisation parameters are adaptively calculated. Then, the ground clutter in the radar echo under the precipitation mode is identified and suppressed by the trained FNN. Experimental results of measured data show that the proposed method can be adapted to the specific clutter environment so that the ground clutter can be effectively suppressed in complex weather conditions.