For responding to these challenges, this research presents a multi-channel deep learning time–frequency feature filter framework (DL-TFF). Firstly, we observed ...
Suppressing varying ionosphere clutter and exploring obscured targets are challenging tasks for high frequency surface wave radar (HFSWR). For responding to ...
Experimental results on real HFSWR data sets have demonstrated that DL-TFF can remove varying ionosphere clutter and simultaneously reveal covered targets.
A novel ionospheric clutter mitigation method through time-frequency image processing based on ridgelet analysis · Cascaded method for ionospheric ...
Deep Learning Aided Time–Frequency Analysis Filter Framework for Suppressing Ionosphere Clutter ; Carrier frequency. 4.7 MHz ; Coherent Integration Time. 144 s.
Deep Learning Aided Time-Frequency Analysis Filter ... - dblp
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Bibliographic details on 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 ...
by: Yang Yunlong, et al. Published: (2016-12-01); Deep Learning Aided Time–Frequency Analysis Filter Framework for Suppressing Ionosphere Clutter by: Xiaowei ...
Article "Deep Learning Aided TimeFrequency Analysis Filter Framework for Suppressing Ionosphere Clutter" Detailed information of the J-GLOBAL is an ...
The problem that this paper is concerned with is High Frequency Surface Wave Radar (HFSWR) detection of desired targets against a complex interference ...