Hard Sample Meta-Learning for CIR NLOS Identification in UWB Positioning
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2025.3525722
Abstract: Nonline-of-sight (NLOS) identification is the key technique to improve the accuracy of the channel impulse response (CIR)-based ultrawideband (UWB) positioning system. However, most existing NLOS identification approaches are tailored to static environments and often encounter… read more here.
Keywords: nlos identification; hard sample; sample meta; identification ... See more keywords
DHNet: Salient Object Detection With Dynamic Scale-Aware Learning and Hard-Sample Refinement
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DOI: 10.1109/tcsvt.2022.3183641
Abstract: During the annotation procedure of salient object detection, researchers usually locate the approximate location of the salient objects first and then process the pixels that need to be finely annotated. Following this idea, we find… read more here.
Keywords: hard sample; scale aware; salient object; dynamic scale ... See more keywords