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Found 15 papers, 4 papers with code
Adaptive Basis Function Selection for Computationally Efficient Predictions
Basis Function (BF) expansions are a cornerstone of any engineer's toolbox for computational function approximation which shares connections with both neural networks and Gaussian processes.
Joint State and Parameter Estimation Using the Partial Errors-in-Variables Principle
This letter proposes a new method for joint state and parameter estimation in uncertain dynamical systems.
Matrix-Valued Measures and Wishart Statistics for Target Tracking Applications
Ensuring sufficiently accurate models is crucial in target tracking systems.
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.
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.
Using Received Power in Microphone Arrays to Estimate Direction of Arrival
Conventional direction of arrival (DOA) estimators are based on array processing using either time differences or beamforming.
Track-To-Track Association for Fusion of Dimension-Reduced Estimates
In this work we propose a novel problem formalization where estimates are reduced based on association performance.
Iterated Filters for Nonlinear Transition Models
A new class of iterated linearization-based nonlinear filters, dubbed dynamically iterated filters, is presented.
Decentralized State Estimation In A Dimension-Reduced Linear Regression
The exchanged estimates are dimension-reduced to reduce the communication load using a linear mapping to a lower-dimensional space.
Framework for Network-Constrained Tracking of Cyclists and Pedestrians
This paper proposes a new method for advanced traffic applications, tracking an unknown and varying number of moving targets (e. g., pedestrians or cyclists) constrained by a road network, using mobile (e. g., vehicles) spatially distributed sensor platforms.
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.
PMBM filter with partially grid-based birth model with applications in sensor management
Whereas conventional PMBM filter implementations typically use Gaussian mixtures to model the intensity of undetected targets, the proposed representation allows the intensity to vary over the region of interest with sharp edges around the sensor's field of view, without using a large number of Gaussian mixture components.
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.
Asynchronous Averaging of Gait Cycles for Classification of Gait and Device Modes
An approach for computing unique gait signature using measurements collected from body-worn inertial measurement units (IMUs) is proposed.
