Riemannian distance based generalized side-lobe cancellation method for nonstationary clutter suppression

High-frequency mixed-mode surface wave radar combines advantages of both surface wave radar and sky wave radar and is capable of ocean remote sensing and early warning. However, the non-stationary clutter is the biggest challenge for the practical radar system which can submerge the low velocity vessels. Generalized side-lobe cancellation method has been successfully developed to suppress the clutter in high-frequency radar, but the performance of the method degrades in heterogeneous environment for the clutter statistics changes significantly. The invalid training data degrades the clutter suppression performance. To suppress the non-stationary clutter efficiently, a Riemannian distance based generalized side-lobe cancellation method is proposed to select the valid training data and improve the performance. Firstly, a single notch filter and virtual sliding sub-array method are utilized to block the main-lobe component. Then, a Riemannian distance based training data selection strategy is proposed to suppress the non-stationary clutter. The validity of the proposed method has been examined by measured data.