Deep Learning Aided Time-Frequency Analysis Filter Framework for Suppressing Ionosphere Clutter

In a heterogeneous environment, the ionosphere is dynamically changing in the Earth's middle latitude, and backscatter from fast-moving irregularities in the plasma can cause ionosphere clutter to extend. Suppressing varying ionosphere clutter and exploring obscured targets are challenging tasks for high frequency surface wave radar (HFSWR). For responding to these challenges, this research presents a multi-channel deep learning time-frequency feature filter framework (DL-TFF). Firstly, we observed the behavior of the ionosphere clutter for a long period of time before selecting the representative ionosphere clutter. Secondly, different transform techniques are applied to provide a time-frequency representation of the non-stationary echo signals, and representation results of different echo components are collected as a training set for feature learning. Thirdly, we design a multi-channel time-frequency feature learning network (MTF), which is responsible for mining discriminative time-frequency information between targets and different types of ionosphere clutter. Experimental results on real HFSWR data sets have demonstrated that DL-TFF can remove varying ionosphere clutter and simultaneously reveal covered targets. Moreover, its suppression effectiveness is more ideal than the previous classical method.