Acoustic node calibration using helicopter sounds and Monte-Carlo Markov chain methods

Author

Cevher, Volkan ; McClellan, James H.

Author_Institution

Georgia Inst. of Technol., Atlanta, GA, USA

fYear

2004

fDate

1-4 Aug. 2004

Firstpage

347

Lastpage

351

Abstract

A Monte-Carlo method is used to calibrate a randomly placed sensor node using helicopter sounds. The calibration is based on using GPS information from the helicopter and the estimated DOAs at the node. The related Cramer-Rao lower bound is derived and the effects of the GPS errors on the position estimates are derived. Issues related to the processing of the field data, e.g., time synchronization and data nonstationarity, are discussed. The effects of the GPS errors are shown to be negligible under certain conditions. Finally, the results of the calibration on field data are given.

Keywords

Global Positioning System; Markov processes; Monte Carlo methods; acoustic arrays; acoustic signal processing; array signal processing; calibration; direction-of-arrival estimation; synchronisation; target tracking; Cramer-Rao lower bound; DOA estimation; GPS information; Monte-Carlo Markov chain methods; Monte-Carlo method; acoustic arrays; acoustic node calibration; data nonstationarity; helicopter sounds; position estimation; sensor node; target tracking; time synchronization; Acoustic arrays; Acoustic measurements; Calibration; Delay estimation; Direction of arrival estimation; Estimation error; Global Positioning System; Helicopters; Sampling methods; Yttrium;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Signal Processing Workshop, 2004 and the 3rd IEEE Signal Processing Education Workshop. 2004 IEEE 11th

Print_ISBN

0-7803-8434-2

Type

conf

DOI

10.1109/DSPWS.2004.1437973

Filename

1437973