DocumentCode :
1755382
Title :
The Cauchy–Schwarz Divergence for Poisson Point Processes
Author :
Hung Gia Hoang ; Ba-Ngu Vo ; Ba-Tuong Vo ; Mahler, Ronald
Author_Institution :
Dept. of Electr. & Comput. Eng., Curtin Univ., Bentley, WA, Australia
Volume :
61
Issue :
8
fYear :
2015
fDate :
Aug. 2015
Firstpage :
4475
Lastpage :
4485
Abstract :
In this paper, we extend the notion of Cauchy-Schwarz divergence to point processes and establish that the Cauchy-Schwarz divergence between the probability densities of two Poisson point processes is half the squared L2-distance between their intensity functions. Extension of this result to mixtures of Poisson point processes and, in the case where the intensity functions are Gaussian mixtures, closed form expressions for the Cauchy-Schwarz divergence are presented. Our result also implies that the Bhattacharyya distance between the probability distributions of two Poisson point processes is equal to the square of the Hellinger distance between their intensity measures. We illustrate the result via a sensor management application where the system states are modeled as point processes.
Keywords :
Gaussian processes; probability; sensors; Bhattacharyya distance; Cauchy-Schwarz divergence; Gaussian mixture; Hellinger distance; Poisson point process; closed form expression; intensity function; probability density; probability distribution; sensor management application; Atmospheric measurements; Density measurement; Measurement units; Particle measurements; Probability distribution; Random variables; Standards; Poisson point process; information divergence; random finite sets;
fLanguage :
English
Journal_Title :
Information Theory, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9448
Type :
jour
DOI :
10.1109/TIT.2015.2441709
Filename :
7118202
Link To Document :
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