Results 21 to 30 of about 1,207 (171)

Robust Poisson Multi-Bernoulli Mixture Filter With Inaccurate Process and Measurement Noise Covariances

IEEE Access, 2020
This paper proposes a robust Poisson multi-Bernoulli mixture (PMBM) filter with inaccurate process and measurement noise covariances. A derivation of the robust PMBM filter is provided for jointly estimating the kinematic state, the predicted state ...
Wenjuan Li, Hong Gu, Weimin Su
doaj   +1 more source

Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance

Sensors, 2021
Assuming that the measurement and process noise covariances are known, the probability hypothesis density (PHD) filter is effective in real-time multi-target tracking; however, noise covariance is often unknown and time-varying for an actual scene.
Zhentao Hu, Linlin Yang, Yong Jin, Han Wang, Shibo Yang   +4 more
doaj   +1 more source

Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter

Sensors, 2020
The existence of clutter, unknown measurement sources, unknown number of targets, and undetected probability are problems for multi-extended target tracking, to address these problems; this paper proposes a gamma-Gaussian-inverse Wishart (GGIW ...
Haocui Du, Weixin Xie
doaj   +1 more source

Implementation of the Gamma Gaussian Inverse Wishart Trajectory Probability Hypothesis Density Filter [PDF]

, 2021
This report contains equations used in the Gamma Gaussian Inverse Wishart Trajectory Probability Hypothesis Density (GGIWTPHD ...
Marcusson, Martin, Sjudin, Jakob
core  

Hierarchical Bayesian Local Gaussian Mixture Model for Image Restoration

Jisuanji kexue yu tansuo, 2020
In recent years, Bayesian approach using Gaussian model as a patch prior has achieved great success in image denoising. However, this approach is not stable in solving inverse problems beyond denoising.
ZHANG Mohua, PENG Jianhua
doaj   +1 more source

A Novel MS-MeMBer Filter for Extended Targets Tracking

IEEE Access, 2020
Conventional multi-sensor multi-target multi-Bernoulli (MS-MeMBer) filters are based on the assumption that each target produces at most one measurement per time step.
Zhiguo Zhang, Jinping Sun, Qing Li, Chao Liu, Guanhua Ding   +4 more
doaj   +1 more source

Variational Low-Rank Matrix Factorization with Multi-Patch Collaborative Learning for Hyperspectral Imagery Mixed Denoising

Remote Sensing, 2021
In this study, multi-patch collaborative learning is introduced into variational low-rank matrix factorization to suppress mixed noise in hyperspectral images (HSIs).
Shuai Liu, Jie Feng, Zhiqiang Tian
doaj   +1 more source

Predictive distributions of random variables following a multivariate Gaussian distribution with Normal Inverse Wishart prior: Technical report

, 2021
The Gaussian distribution is a popular choice of density for the modeling of continuous observations, often combined with a specific prior in the context of Bayesian inference.
Goffinet, Etienne, Lebbah, Mustapha, Giraldi, Loïc, Azzag, Hanane   +3 more
core   +1 more source

Adaptive probability hypothesis density filter for multi-target tracking with unknown measurement noise statistics

Measurement + Control, 2021
Under the Gaussian noise assumption, the probability hypothesis density (PHD) filter represents a promising tool for tracking a group of moving targets with a time-varying number.
Weijun Xu
doaj   +1 more source

An Improved Invariant Kalman Filter for Lie Groups Attitude Dynamics with Heavy-Tailed Process Noise

Machines, 2021
Attitude estimation is a basic task for most spacecraft missions in aerospace engineering and many Kalman type attitude estimators have been applied to the guidance and navigation of spacecraft systems.
Jiaolong Wang, Chengxi Zhang, Jin Wu, Ming Liu   +3 more
doaj   +1 more source