Distributed Adaptive Parametric Power Spectral Estimation Using Wireless Sensor Networks

Hamed Nosrati, Sayed Mostafa Taheri, Mousa Shamsi, Mohammad H. Sedaaghi

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

Abstract

Spectrum analysis is one of the momentous fields in signal processing. It has a large variety of applications in radar, sonar, speech and image processing. Parametric methods have been proposed and employed for spectrum analysis including power spectral density (PSD) estimation. These methods estimate the parameters of a statistical model and compute the PSD, afterwards. In some circumstances one is obliged to deal with observations of numerous geographically dispersed sensors, to either increase the precision or based on application demands. Having a set of sensors linked together to take the advantages of cooperation and network topology, one obtains a more comprehensive estimation. In this chapter, the authors propose and study four different algorithms capable of facing spatio-temporal variations for parametric modeling and PSD estimation using wireless sensor networks (WSNs). For this purpose, the authors first validate the proposed algorithms using theoretical and mathematical formulations. Thereafter, performing simulation tasks demonstrates and supports the theoretical achievements. The next section of the chapter illustrates the concepts to a greater degree, the authors analyze and compare the performance of these algorithms with each other, as well as with the simple PSD estimation using individual sensors, wherein there is no cooperation among the nodes.
Original languageEnglish
Title of host publicationTechnological Breakthroughs in Modern Wireless Sensor Applications
EditorsHamid Sharif, Yousef S. Kavian
PublisherIGI Global
Pages321-351
ISBN (Electronic)9781466682528
ISBN (Print)9781466682511
DOIs
Publication statusPublished - 2015
Externally publishedYes

Fingerprint

Dive into the research topics of 'Distributed Adaptive Parametric Power Spectral Estimation Using Wireless Sensor Networks'. Together they form a unique fingerprint.

Cite this