Title :
Performance of PHD Based Multi-Target Filters
Author :
Vo, Ba-Tuong ; Vo, Ba-Ngu ; Cantoni, Antonio
Author_Institution :
Western Australian Telecommun. Res. Inst., Western Australia Univ., Crawley, WA
Abstract :
The probability hypothesis density (PHD) recursion is a first moment approximation to the multi-target Bayes filter which propagates the posterior intensity of the random finite set of targets in time. The cardinalized PHD (CPHD) recursion is a generalization of the PHD recursion, which jointly propagates the posterior intensity and the posterior cardinality distribution. Using the recently developed closed form solutions to both the PHD and CPHD recursions for linear Gaussian multi-target models, we present a comparative study of their performances
Keywords :
Bayes methods; Gaussian distribution; approximation theory; recursive filters; target tracking; tracking filters; cardinalized PHD recursion; linear Gaussian models; moment approximation; multitarget Bayes filters; posterior cardinality distribution; probability hypothesis density; Australia; Closed-form solution; Information filtering; Information filters; Optical propagation; State estimation; Statistics; Target tracking; Time measurement; Uncertainty; cardinalized probability hypothesis density filter; multi-target Bayes filter; multi-target tracking; probability hypothesis density filter; random finite sets;
Conference_Titel :
Information Fusion, 2006 9th International Conference on
Conference_Location :
Florence
Print_ISBN :
1-4244-0953-5
Electronic_ISBN :
0-9721844-6-5
DOI :
10.1109/ICIF.2006.301606
