TY - GEN
T1 - Information Fusion Using Particles Intersection
AU - Tslil, Or
AU - Carmi, Avishy
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - A technique is presented for combining arbitrary empirical probability density estimates whose interdependencies are unspecified. The underlying estimates may be, for example, the particle approximations of a pair of particle filters. In this respect, our approach provides a way to obtain a new particle approximation, which is better in a precise information-theoretic sense than that of any of the particle filters alone. The viability of the proposed approach is demonstrated in a multiple object tracking scenario.
AB - A technique is presented for combining arbitrary empirical probability density estimates whose interdependencies are unspecified. The underlying estimates may be, for example, the particle approximations of a pair of particle filters. In this respect, our approach provides a way to obtain a new particle approximation, which is better in a precise information-theoretic sense than that of any of the particle filters alone. The viability of the proposed approach is demonstrated in a multiple object tracking scenario.
UR - http://www.scopus.com/inward/record.url?scp=85054285588&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2018.8461348
DO - 10.1109/ICASSP.2018.8461348
M3 - Conference contribution
AN - SCOPUS:85054285588
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4269
EP - 4273
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Y2 - 15 April 2018 through 20 April 2018
ER -