Title :
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
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;
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2010.5495862
