Search results for "Kalman Filter"
showing 10 items of 108 documents
Convergence analysis of cubature Kalman filter
2014
This paper investigates the stability analysis of cubature Kalman filter (CKF) for nonlinear systems with linear measurement. The certain conditions to ensure that the estimation error of CKF remains bounded are proved. Then, the effect of process noise covariance is investigated and an adaptive process noise covariance is proposed to deal with large estimation error. Accordingly, a modified CKF (MCKF) is developed to enhance the stability and accuracy of state estimation. The performance of the MCKF is compared to the CKF by two case studies. Simulation results demonstrate that the large estimation error may lead to instability of CKF while the MCKF is successfully able to estimate the sta…
Sustainable Method Using Filtering Techniques for a Fermentation Process State Estimation
2020
Winemaking is concerned about sustainable energy availability that implies new methods for process monitoring and control. The aim of this paper is to realize a comparative analysis of the possibilities offered using estimation techniques, balances, and filtering techniques such as the Kalman filter (KF) and the extended Kalman filter (EKF), to obtain indirect information about the alcoholic fermentation process during winemaking. Thus, an estimation solution of the process variables in the exponential growing phase is proposed, using an extended observer. In addition, two estimation solutions of this process with the EKF and an estimation of the decay phase of the fermentation process are …
Online Edge Flow Imputation on Networks
2022
Author's accepted manuscript © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. An online algorithm for missing data imputation for networks with signals defined on the edges is presented. Leveraging the prior knowledge intrinsic to real-world networks, we propose a bi-level optimization scheme that exploits the causal dependencies and the flow conservation, respe…
Event reconstruction methods for the HypHI Phase 0 experiment at GSI
2010
WOS: 000282530300034
Kalman filter tracking and vertexing in a silicon detector for neutrino physics
2002
Abstract This article describes the application of Kalman filter techniques for the tracking and vertexing of particles inside the NOMAD-STAR detector, a silicon vertex detector installed in NOMAD, one of the neutrino oscillation experiments at the CERN-SPS. The use of the Kalman filter simplifies computationally the tracking and vertex procedure for NOMAD-STAR. The alignment of NOMAD-STAR is shown as an example of the application of the Kalman filter for tracking purposes. The accuracy of the method is such that one obtains alignment residuals between 9 and 12 μm . Furthermore, a preliminary measure of the impact parameter (with an RMS ∼36 μm ) illustrates the vertexing capabilities of thi…
“RecPack” a reconstruction toolkit
2004
We present a C++ toolkit to do tracking and vertex reconstruction. The toolkit incorporates common fitting methods, as the Kalman Filter, a framework to define a detector setup, a general navigation and a simple simulation. Furthermore, the toolkit provides a collection of interfaces which facilitates the addition of new fitting methods, trajectory models, geometrical objects, pattern recognition logic, etc. Although the toolkit was originally developed to be used in High Energy Physics, it could be applied to other fields.
Invariant pattern recognition based on 1-D Wavelet functions and the polynomial decomposition
1997
Abstract A new filter, consisting of 1-D Wavelet functions is suggested for achieving optical invariant pattern recognition. The formed filter is actually a real function, hence, it is theoretically possible to be implemented under both spatially coherent and spatially incoherent illuminations. The filter is based on the polynomial expansion, and is constructed out of a scaled bank of filters multiplied by 1-D Wavelet weight functions. The obtained output is shown to be invariant to 2-D scaling even when different scaling factors are applied on the different axes. The computer simulations and the experimental results demonstrate the potential hidden in this technique.
Sensorless Control of Induction-Motor Drive Based on Robust Kalman Filter and Adaptive Speed Estimation
2014
This paper deals with robust estimation of rotor flux and speed for sensorless control of motion control systems with an induction motor. Instead of using sixth-order extended Kalman filters (EKFs), rotor flux is estimated by means of a fourth-order descriptor-type robust KF, which explicitly takes into account motor parameter uncertainties, whereas the speed is estimated using a recursive least squares algorithm starting from the knowledge of the rotor flux itself. It is shown that the descriptor-type structure allows for a direct translation of parameter uncertainties into variations of the coefficients appearing in the model, and this improves the degree of robustness of the estimates. E…
Solving Non-Stationary Bandit Problems by Random Sampling from Sibling Kalman Filters
2010
Published version of an article from Lecture Notes in Computer Science. Also available at SpringerLink: http://dx.doi.org/10.1007/978-3-642-13033-5_21 The multi-armed bandit problem is a classical optimization problem where an agent sequentially pulls one of multiple arms attached to a gambling machine, with each pull resulting in a random reward. The reward distributions are unknown, and thus, one must balance between exploiting existing knowledge about the arms, and obtaining new information. Dynamically changing (non-stationary) bandit problems are particularly challenging because each change of the reward distributions may progressively degrade the performance of any fixed strategy. Alt…
Relative Vessel Motion Tracking using Sensor Fusion, Aruco Markers, and MRU Sensors
2017
This paper presents a novel approach for estimating the relative motion between two moving offshore vessels. The method is based on a sensor fusion algorithm including a vision system and two motion reference units (MRUs). The vision system makes use of the open-source computer vision library OpenCV and a cube with Aruco markers placed onto each of the cube sides. The Extended Quaternion Kalman Filter (EQKF) is used for bad pose rejection for the vision system. The presented sensor fusion algorithm is based on the Indirect Feedforward Kalman Filter for error estimation. The system is self-calibrating in the sense that the Aruco cube can be placed in an arbitrary location on the secondary ve…