Accession Number : AD1022600

Title :   Learning and Visualizing Modulation Discriminative Radio Signal Features

Descriptive Note : Technical Report

Corporate Author : Space and Naval Warfare Systems Center Pacific San Diego United States

Personal Author(s) : Walton,Michael ; Gebhardt,Daniel ; Migliori,Benjamin ; Straatemeier,Logan

Full Text :

Report Date : 01 Sep 2016

Pagination or Media Count : 24

Abstract : In this work we explore the adaptation of convolutional autoencoders to complex-valued temporal domain radio signals. We propose a method for accomplishing online semi-supervised learning with a tied-weight convolutional autoencoder applied to a modulation classification task and provide some initial results. We also demonstrate a novel application of class activation maps (CAMs) to obtain interpretable visualizations of modulation-discriminative temporal structure in input signals. Finally, we show that our visualization method may be successfully applied to pre-trained models with negligible impact on classification performance on an automated modulation classification (AMC) task. This work was done as part of the BIAS (Biologically Inspired Autonomous Sensing) project, funded from the Naval Innovative Science and Engineering (NISE) Program.

Descriptors :   semisupervised learning , signal processing , radio signals , coders , automation , artificial neural networks

Subject Categories : Operations Research
      Computer Systems Management and Standards

Distribution Statement : APPROVED FOR PUBLIC RELEASE