SIGNAL CLUSTERING WITH CLASS-INDEPENDENT SEGMENTATION

被引：0

作者：

Gasperini, Stefano ^{[1
,2
]}

Paschali, Magdalini ^{[1
]}

Hopke, Carsten ^{[2
]}

Wittmann, David ^{[2
]}

Navab, Nassir ^{[1
,3
]}

机构：

[1] Tech Univ Munich, Comp Aided Med Procedures, Munich, Germany

[2] Airbus Def & Space GmbH, Manching, Germany

[3] Johns Hopkins Univ, Comp Aided Med Procedures, Baltimore, MD USA

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING | 2020年

关键词：

Clustering; Deep Learning; Radar Signals; Image Segmentation; Class-independence;

D O I：

10.1109/icassp40776.2020.9053409

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Radar signals have been dramatically increasing in complexity, limiting the source separation ability of traditional approaches. In this paper we propose a Deep Learning-based clustering method, which encodes concurrent signals into images, and, for the first time, tackles clustering with image segmentation. Novel loss functions are introduced to optimize a Neural Network to separate the input pulses into pure and non-fragmented clusters. Outperforming a variety of baselines, the proposed approach is capable of clustering inputs directly with a Neural Network, in an end-to-end fashion.

引用

页码：3982 / 3986

页数：5

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