Radar emitter and activity identification using deep clustering methods

Diego Marez; Samuel Borden; Gregori Clarke; John Reeder; Nicholas Johnson

doi:10.1117/12.2517864

10 May 2019 Radar emitter and activity identification using deep clustering methods

Diego Marez, Samuel Borden, Gregori Clarke, John Reeder, Nicholas Johnson

Proceedings Volume 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications; 110061H (2019) https://doi.org/10.1117/12.2517864
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Current challenges in spectrum monitoring include radar emitter state identification and the ability to detect changes in radar activity. Recently, large labeled datasets and better compute power have led to improvements in the classification performance of deep neural network (DNN) models on structured data like time series and images. The reliance on large labeled dataset in order to achieve state of the art performance is a hindrance for machine learning applications especially in the area of radar, which tends to have a wealth of noisy and unlabeled data. Due to the abundance of unlabeled data, the problem of radar emitter and activity identification is commonly setup as a clustering problem, which requires no labels. The deep clustering approach uses an underlying deep feature extractor such as an autoencoder to learn a low dimensional feature representation in the service of facilitating a clustering task. In this paper, we will evaluate different clustering loss functions such as K-means for training DNNs, and we use radar emitter state and activity identification as our example task.

Conference Presentation

Citation Download Citation

Diego Marez, Samuel Borden, Gregori Clarke, John Reeder, and Nicholas Johnson "Radar emitter and activity identification using deep clustering methods", Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110061H (10 May 2019); https://doi.org/10.1117/12.2517864

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