Add to ORKGManeuvering target tracking is a challenging problem for sensor systems because of the unpredictability of the targets' motions. This paper proposes a novel data-driven method for learning the dynamical motion model of a target. Non-parametric Gaussian process regression (GPR) is used to learn a target's naturally shift invariant motion (NSIM) behavior, which is translationally invariant and does not need to be constantly updated as the target moves. The learned Gaussian processes (GPs) can be applied to track targets within different surveillance regions from the surveillance region of the training data by being incorporated into the particle filter (PF) implementation. The performance of our proposed approach is evaluated over different m...
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
This paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear time series analysis. A...
This article proposes a Gaussian filtering method to approximate the single-target updates and norma...
Manoeuvring target tracking faces the challenge caused by the target motion model uncertainty, i.e.,...
Model-based approaches for target tracking and smoothing estimate the infinite number of possible ta...
The application of multiple target tracking algorithms has exponentially increased during the last t...
Target tracking performance relies on the match between the tracker motion model and the unknown tar...
The application of multiple target tracking algorithms has exponentially increased during the last t...
The most difficult—and often most essential— aspect of many interception and tracking tasks is cons...
Tracked targets often exhibit common behaviors due to influences from the surrounding environment, s...
Tracked targets often exhibit common behaviors due to influences from the surrounding environment, s...
<p>Bayesian nonparametric models, such as the Gaussian process and the Dirichlet process, have been ...
A new mathematical model describing the motion of manned maneuvering targets is presented. This mode...
Target tracking is crucial in monitoring and controlling air traffic in civilian and military applic...
AbstractIn this paper, an improved implementation of multiple model Gaussian mixture probability hyp...
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
This paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear time series analysis. A...
This article proposes a Gaussian filtering method to approximate the single-target updates and norma...
Manoeuvring target tracking faces the challenge caused by the target motion model uncertainty, i.e.,...
Model-based approaches for target tracking and smoothing estimate the infinite number of possible ta...
The application of multiple target tracking algorithms has exponentially increased during the last t...
Target tracking performance relies on the match between the tracker motion model and the unknown tar...
The application of multiple target tracking algorithms has exponentially increased during the last t...
The most difficult—and often most essential— aspect of many interception and tracking tasks is cons...
Tracked targets often exhibit common behaviors due to influences from the surrounding environment, s...
Tracked targets often exhibit common behaviors due to influences from the surrounding environment, s...
<p>Bayesian nonparametric models, such as the Gaussian process and the Dirichlet process, have been ...
A new mathematical model describing the motion of manned maneuvering targets is presented. This mode...
Target tracking is crucial in monitoring and controlling air traffic in civilian and military applic...
AbstractIn this paper, an improved implementation of multiple model Gaussian mixture probability hyp...
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
This paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear time series analysis. A...
This article proposes a Gaussian filtering method to approximate the single-target updates and norma...