A Bayesian Approach to Spectrum Sensing, Denoising and Anomaly Detection
2009 (English)In: Proceedings of the 34th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'09), 2009, 2333-2336 p.Conference paper (Refereed)
This paper deals with the problem of discriminating samples that contain only noise from samples that contain a signal embedded in noise. The focus is on the case when the variance of the noise is unknown. We derive the optimal soft decision detector using a Bayesian approach. The complexity of this optimal detector grows exponentially with the number of observations and as a remedy, we propose a number of approximations to it. The problem under study is a fundamental one and it has applications in signal denoising, anomaly detection, and spectrum sensing for cognitive radio. We illustrate the results in the context of the latter.
Place, publisher, year, edition, pages
2009. 2333-2336 p.
, Acoustics, Speech and Signal Processing, ISSN 1520-6149 ; 2009
National CategoryEngineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-25592DOI: 10.1109/ICASSP.2009.4960088ISBN: 978-1-4244-2354-5ISBN: 978-1-4244-2353-8OAI: oai:DiVA.org:liu-25592DiVA: diva2:246031
34th IEEE international conference on acoustics, speech and signal processing,19-24 April, Taipei, Taiwan
©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Erik Axell and Erik G. Larsson, A Bayesian Approach to Spectrum Sensing, Denoising and Anomaly Detection, 2009, Proceedings of the 34th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'09), 2333-2336. http://dx.doi.org/10.1109/ICASSP.2009.49600882009-10-082009-10-082012-07-10Bibliographically approved