Title :
Robustness Analysis of Source Localization Using Gaussianity Measure
Author :
Yan, Kun ; Wu, Hsiao-Chun ; Iyengar, S.S.
Author_Institution :
Dept. of Electr. & Comput. Eng., Louisiana State Univ., Baton Rouge, LA
Abstract :
Nowadays, the source localization has been widely applied for wireless sensor networks. The Gaussian mixture model has been adopted for maximum-likelihood (ML) source localization schemes. However, this model does not match the statistics of the real data in practice. In this paper, we study the probability density function of the sensor signals and demonstrate that the distribution is not Gaussian. We propose to employ the Gaussianity test based on the bootstrap algorithm to quantify the departure of Gaussianity for the received signals added with different kinds of noise. Our proposed Gaussianity test can be used as the robustness figure for evaluating the prevalent ML source localization schemes.
Keywords :
Gaussian processes; maximum likelihood estimation; signal processing; statistical distributions; statistical testing; wireless sensor networks; Gaussianity measure; bootstrap algorithm; maximum-likelihood source localization; probability density function; robustness analysis; sensor signal; statistical distribution; wireless sensor network; Gaussian noise; Gaussian processes; Robustness; Sensor arrays; Signal processing; Signal processing algorithms; Statistics; Testing; Wideband; Wireless sensor networks;
Conference_Titel :
Global Telecommunications Conference, 2008. IEEE GLOBECOM 2008. IEEE
Conference_Location :
New Orleans, LO
Print_ISBN :
978-1-4244-2324-8
DOI :
10.1109/GLOCOM.2008.ECP.622
