Results 201 to 210 of about 176,655 (248)

Statistics of multiscale fragmentation in the Primorsky fault zone. [PDF]

Sci Rep
Ostapchuk A, Chinkin V, Grigorieva A, Pavlov D.   +3 more
europepmc   +1 more source

How Interviewees Determine What Interviewers Want to Know. [PDF]

Int Rev Soc Psychol
Neequaye DA, Lorson A, Barnett HK.
europepmc   +1 more source

Integrating molecular dynamics simulations to enable rational assembly of immune signals for immunotherapy.

Nanoscale Horiz
Froimchuk E, Jain M, Sahoo A, Edwards C, Shah S, Oakes RS, Matysiak S, Jewell CM.   +7 more
europepmc   +1 more source

Weighted Chernoff Information and Optimal Loss Exponent in Context-Sensitive Hypothesis Testing. [PDF]

Entropy (Basel)
Kelbert M, Kalimulina EY.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

A physical-space approach for the probability hypothesis density and cardinalized probability hypothesis density filters

SPIE Proceedings, 2006
The probability hypothesis density (PHD) filter, an automatically track-managed multi-target tracker, is attracting increasing but cautious attention. Its derivation is elegant and mathematical, and thus of course many engineers fear it; perhaps that is currently limiting the number of researchers working on the subject. In this paper, we explore
Peter Willett, Yaakov Bar-Shalom, Özgur Erdinc   +2 more
exaly   +2 more sources

Forward-Backward Probability Hypothesis Density Smoothing

IEEE Transactions on Aerospace and Electronic Systems, 2012
A forward-backward probability hypothesis density (PHD) smoother involving forward filtering followed by backward smoothing is proposed. The forward filtering is performed by Mahler's PHD recursion. The PHD backward smoothing recursion is derived using finite set statistics (FISST) and standard point process theory.
Ba Tuong Võ, Ronald Mahler, Ba-Ngu Võ   +2 more
exaly   +3 more sources

Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter

IEEE Transactions on Signal Processing, 2007
The probability hypothesis density (PHD) recursion propagates the posterior intensity of the random finite set (RFS) 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.
Ba Tuong Võ, Ba-Ngu Võ, Antonio Cantoni   +2 more
exaly   +2 more sources

Data-Driven Probability Hypothesis Density Filter for Visual Tracking

IEEE Transactions on Circuits and Systems for Video Technology, 2008
We apply the probability hypothesis density (PHD) filter to track a random number of pedestrians in image sequences. The PHD filter is implemented using particle filter. How to design importance functions of the particle PHD filter remains a challenge, especially when targets can appear, disappear, merge, or split at any time.
Ashraf Kassim, Jian-Kang Wu, A A Kassim   +2 more
exaly   +2 more sources

Probability hypothesis density filter with uncertainty in the probability of detection

Advances in Space Research, 2021
Abstract The space around the earth is becoming increasingly populated. Efficient tracking algorithms are hence integral to protect active space assets from collisions. Ground-based measurements are the primary source of information for any tracking algorithm.
Rohith Reddy Sanaga, Carolin Frueh
openaire   +1 more source