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Deep Attention Aware Feature Learning for Data Association in Multiple Source Localization
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DOI：10.1109/LCOMM.2022.3215721.png)
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摘要
En 中文
The letter addresses the problem of data association for multiple source localization in a distributed sensor network. To increase the efficiency of localization, we present a learning methodology to generate highly distinctive measurement features for data association. We propose an attention-based graph neural network that exploits the relationship among the phases from the Short Time Fourier transform and the estimated measurements to generate highly descriptive features for each of the detected source signals. The methodology is free from any selective threshold and processes each of the measurements to detect and enrich the similar and distinguishable aspects of the measurements. These aspects are then aggregated in feature representations for each source measurement. The generated features can then be employed with any data association method to segregate the measurements belonging to the same source. It is observed in experimental evaluation that the proposed features employed with state-of-the-art association frameworks yield lower association and localization errors.
Keyword:
Feature extraction
Phase measurement
Location awareness
Manganese
Weight measurement
Time-frequency analysis
Oceanography
Multiple source location estimation
data association
Index Terms
direction of arrival
time-delay of arrival
deep neural networks
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
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