Exponentially embedded families for multimodal sensor processing

Author

Kay, Steven ; Ding, Quan

Author_Institution

Dept. of Electr., Comput., & Biomed. Eng., Univ. of Rhode Island, Kingston, RI, USA

fYear

2010

fDate

14-19 March 2010

Firstpage

3770

Lastpage

3773

Abstract

The exponential embedding of two or more probability density functions (PDFs) is proposed for multimodal sensor processing. It approximates the unknown PDF by exponentially embedding the known PDFs. Such embedding is of a exponential family indexed by some parameters, and hence inherits many nice properties of the exponential family. It is shown that the approximated PDF is asymptotically the one that is the closest to the unknown PDF in Kullback-Leibler (KL) divergence. Applied to hypothesis testing, this approach shows improved performance compared to existing methods for cases of practical importance where the sensor outputs are not independent.

Keywords

probability; sensor fusion; sensors; signal processing; Kullback-Leibler divergence; exponential embedding; exponentially embedded family; hypothesis testing; multimodal sensor processing; probability density functions; sensor fusion; Biomedical computing; Biomedical engineering; Embedded computing; Meteorological radar; Multimodal sensors; Probability density function; Radar detection; Sensor fusion; Sonar detection; Testing; Kullback-Leibler divergence; Sensor fusion; exponential embedding; exponential family; hypothesis testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on

Conference_Location

Dallas, TX

ISSN

1520-6149

Print_ISBN

978-1-4244-4295-9

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2010.5495862

Filename

5495862