Spectrum Alerting System Based on Software Defined Radio and Raspberry Pi

David Ball, Nitin Naik, Paul Jenkins

Research output: Chapter in Book/Published conference outputConference publication

Abstract

This paper presents the design and implementation of an inexpensive and generic Spectrum Alerting System (SAS), based on Software Defined Radio (SDR), by using the RTL-SDR USB device and a low- power microprocessor Raspberry Pi. The main aim of the SAS is within a defence surveillance system for discovering rogue or unidentified signals in an attentive spectrum, sending alerts to the signals intelligence analyst. However, it can be employed in any kind of intelligence surveillance system. In this implementation, the SAS analyses a predetermined part of the electromagnetic spectrum to establish a baseline pattern, subsequently, monitoring the spectrum to discover rogue or unidentified signals within this spectrum. Detected rogue or unidentified signals are recorded and an email alert message is sent to the signals intelligence analyst for further investigation. Whilst currently, this message is an email, the alert message could be sent via other means such as Twitter, Facebook or other messaging agents. Moreover, the implementation and experimental results demonstrate the success and potential of the SAS as an inexpensive and generic choice for an intelligence surveillance system in both a benign and hostile environment.

Original languageEnglish
Title of host publication2017 Sensor Signal Processing for Defence Conference, SSPD 2017
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)9781538616635
DOIs
Publication statusPublished - 20 Dec 2017
Event7th Conference of the Sensor Signal Processing for Defence, SSPD 2017 - London, United Kingdom
Duration: 6 Dec 20177 Dec 2017

Publication series

Name2017 Sensor Signal Processing for Defence Conference, SSPD 2017
Volume2017-January

Conference

Conference7th Conference of the Sensor Signal Processing for Defence, SSPD 2017
CountryUnited Kingdom
CityLondon
Period6/12/177/12/17

Keywords

  • FFT
  • Raspberry Pi
  • RTL-SDR
  • SAS
  • Signals Intelligence Analyst
  • Software Defined Radio
  • Spectrum Alerting System

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