Search Results for author:
Found 11 papers, 2 papers with code
Extended target tracking utilizing machine-learning software -- with applications to animal classification
This paper considers the problem of detecting and tracking objects in a sequence of images.
Fusion framework and multimodality for the Laplacian approximation of Bayesian neural networks
This paper considers the problem of sequential fusion of predictions from neural networks (NN) and fusion of predictions from multiple NN.
On the Relationship Between Iterated Statistical Linearization and Quasi-Newton Methods
This letter investigates relationships between iterated filtering algorithms based on statistical linearization, such as the iterated unscented Kalman filter (IUKF), and filtering algorithms based on quasi-Newton (QN) methods, such as the QN iterated extended Kalman filter (QN-IEKF).
Unified Linearization-based Nonlinear Filtering
This letter shows that the following three classes of recursive state estimation filters: standard filters, such as the extended Kalman filter; iterated filters, such as the iterated unscented Kalman filter; and dynamically iterated filters, such as the dynamically iterated posterior linearization filters; can be unified in terms of a general algorithm.
On the validity of using the delta method for calculating the uncertainty of the predictions from an overparameterized model
The uncertainty in the prediction calculated using the delta method for an overparameterized (parametric) black-box model is shown to be larger or equal to the uncertainty in the prediction of a canonical (minimal) model.
Tightly Integrated Motion Classification and State Estimation in Foot-Mounted Navigation Systems
A framework for tightly integrated motion mode classification and state estimation in motion-constrained inertial navigation systems is presented.
Uncertainty quantification in neural network classifiers -- a local linear approach
Classifiers based on neural networks (NN) often lack a measure of uncertainty in the predicted class.
Iterated Filters for Nonlinear Transition Models
A new class of iterated linearization-based nonlinear filters, dubbed dynamically iterated filters, is presented.
Inertial Navigation Using an Inertial Sensor Array
We present a comprehensive framework for fusing measurements from multiple and generally placed accelerometers and gyroscopes to perform inertial navigation.
Online Joint State Inference and Learning of Partially Unknown State-Space Models
The method uses an extended Kalman filter approach to jointly estimate the state of the system and learn the unknown system dynamics, via the parameters of the basis function expansion.
Fifteen Years of Progress at Zero Velocity: A Review
Fifteen years have passed since the publication of Foxlin's seminal paper "Pedestrian tracking with shoe-mounted inertial sensors".
