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

Abstract

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.

Space-Time Cascaded Processing-Based Adaptive Transient Interference Mitigation for Compact HFSWR

“…The compact HFSWRs require less deployment space and lower manufacturing costs and are more appropriate for installation at the seashore due to covering less space. Up to now, various compact HFSWRs such as WERA using four antennas, OSMAR using eight antennas, and CODAR have represented the most commonly installed systems around the world [17,18]. Therefore, this study is concerned with two aspects of the performance improvement of the transient interference suppression approach: first, to effectively remove the STI and LCI simultaneously.…”