Results 1 to 10 of about 1,010 (137)

Modified prediction partitioning algorithm for Gaussian inverse Wishart probability hypothesis density filter using measurement labeling method [PDF]

Journal of Algorithms & Computational Technology, 2021
The Gaussian inverse Wishart probability hypothesis density filter is a promising approach for tracking multiple extended targets. However, if targets are closely spaced and performing maneuvers, the performance of a Gaussian inverse Wishart probability ...
Peng Li, Junda Qiu, Wenhui Wang, Shuzhi Su   +3 more
doaj   +4 more sources

A Novel Variational Bayesian Method with Unknown Noise for Underwater INS/DVL/USBL Localization [PDF]

Sensors
In the complex underwater environment, it is hard to obtain accurate system noise prior information. If uncertainty system noise model is used in state determination, the precision will decrease.
Haoqian Huang, Chenhui Dong, Yutong Zhang, Shuang Zhang   +3 more
doaj   +2 more sources

Bayesian Clustering Factor Models. [PDF]

Stat Med
ABSTRACT We present a novel framework for concomitant dimension reduction and clustering. This framework is based on a novel class of Bayesian clustering factor models. These models assume a factor model structure where the vectors of common factors follow a mixture of Gaussian distributions.
Shin H, Ferreira MAR, Tegge AN.
europepmc   +2 more sources

An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation

IEEE Journal of Selected Topics in Signal Processing, 2013
This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has been derived by Mahler, and different implementations have been proposed recently.
Christian Lundquist, Karl Granström, Umut Orguner   +2 more
openaire   +6 more sources

Adaptive Measurement Partitioning Algorithm for a Gaussian Inverse Wishart PHD Filter that Tracks Closely Spaced Extended Targets [PDF]

Radioengineering, 2017
Use of the Gaussian inverse Wishart probability hypothesis density (GIW-PHD) filter has demonstrated promise as an approach to track an unknown number of extended targets.
P. Li, H. Ge, J. Yang
doaj   +3 more sources

Robust Bayesian partition for extended target Gaussian inverse Wishart PHD filter

IET Signal Processing, 2014
Extended target Gaussian inverse Wishart PHD filter is a promising filter. However, when the two or more different sized extended targets are spatially close, the simulation results conducted by Granström et al .
Yongquan Zhang, Hongbing Ji
openaire   +3 more sources

Generalised covariance intersection‐Gamma Gaussian Inverse Wishart‐Poisson multi‐Bernoulli Mixture: An intelligent multiple extended target tracking scheme for mobile aquaculture sensor networks

IET Wireless Sensor Systems
Poisson multi‐Bernoulli Mixture (PMBM) filter has been known as an available or practical point and multiple extended target tracking (METT) method. The authors present an improved PMBM filter with adaptive detection probability and adaptive newborn ...
Chunfeng Lv, Jianping Zhu, Zhiguang Peng
doaj   +2 more sources

Shape selection partitioning algorithm for Gaussian inverse Wishart probability hypothesis density filter for extended target tracking

IET Signal Processing, 2016
The Gaussian inverse Wishart probability hypothesis density (GIW‐PHD) filter is a promising approach for tracking an unknown number of extended targets. However, it does not achieve satisfactory performance if targets in different sizes are spatially close and manoeuvring because the partitioning methods are sensitive to manoeuvres.
Peng Li 0076, Hong-Wei Ge, Jinlong Yang 0002, Huanqing Zhang   +3 more
openaire   +3 more sources

Signpost Testing to Navigate the Parameter Space of the Gaussian Graphical Model With High-Dimensional Data. [PDF]

Biom J
ABSTRACT We evaluate the relevance of external quantitative information on the parameter of a Gaussian graphical model from high‐dimensional data. This information comes in the form of a parameter value available from a related knowledge domain or population.
Ruan K, van de Wiel MA, van Wieringen WN.   +2 more
europepmc   +2 more sources

Hierarchical Bayesian Modelling Improves Microstructural Parameter Mapping in Diffusion and Exchange MRI Data. [PDF]

NMR Biomed
Our hierarchical Bayesian modelling (HBM) technique is demonstrated in two neuroimaging diffusion MRI models. When compared with least‐squares (LSQ) minimisation, HBM increased the accuracy, precision, contrast‐to‐noise ratio and parameter map quality in simulated and human data. HBM also resolved local parameter variations associated with white matter
Powell E, Maskery M, Emsley HCA, Parkes LM, Parker GJM, Slator PJ.   +5 more
europepmc   +2 more sources