RESEARCH PAPERS

Kalman filters research papers




A short introduction to kalman filters
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Kalman filtering is a method for recursively updating an estimate ? of the state of a system by processing a succession of measurements Z. After each measurement, a new state estimate is produced by the filter's measurement step. Z and ? do not necessarily have to have the

Bayesian filtering: From Kalman filters to particle filters, and beyond
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ABSTRACT In this self-contained survey/review paper, we systematically investigate the roots of Bayesian filtering as well as its rich leaves in the literature. Stochastic filtering theory is briefly reviewed with emphasis on nonlinear and non-Gaussian filtering. Following the

Sigma-point Kalman filters for integrated navigation
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Abstract Core to integrated navigation systems is the concept of fusing noisy observations from GPS, Inertial Measurement Units (IMU), and other available sensors. The current industry standard and most widely used algorithm for this purpose is the extended

Enhancements to RSS based indoor tracking systems using Kalman filters
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Abstract This paper describes the site survey issues when deploying a wireless local area network (WLAN), the implementation of a location system over the deployed network, and the application of a Kalman filtering algorithm to enhance the tracking performance.

Robot localization and kalman filters
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Abstract The robot localization problem is a key problem in making truly autonomous robots. If a robot does not know where it is, it can be difficult to determine what to do next. In order to localize itself, a robot has access to relative and absolute measurements giving the robot

Comparison of extended and ensemble Kalman filters for data assimilation in coastal area modelling
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SUMMARY Data assimilation in a two-dimensional hydrodynamic model for bays, estuaries and coastal areas is considered. Two different methods based on the Kalman filter scheme are presented. These include (1) an extended Kalman filter in which the error covariance

Sigma-point Kalman filters for probabilistic inference in dynamic state-space models
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This chapter will first describe the broad problem domain addressed by the body of work presented in this thesis. After this, a compact literature overview of related work in the field is given. Finally, a summary of the specific contributions of the work presented in this

Adaptable sensor fusion using multiple Kalman filters.
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Abstract This paper presents an innovative sensor fusion strategy for the positioning of an underwater ROV. The use of multiple Kalman filters makes the system highly adaptable by allowing different combinations of sensors without any modification of the models. This

Kalman filters
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The celebrated Kalman filter, rooted in the state-space formulation of linear dynamical systems, provides a recursive solution to the linear optimal filtering problem. It applies to stationary as well as nonstationary environments. The solution is recursive in that each

Discriminative Training of Kalman Filters.
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ABSTRACT Kalman filters are a workhorse of robotics and are routinely used in state- estimation problems. However, their performance critically depends on a large number of modeling parameters which can be very difficult to obtain, and are often set via significant

Optimal filtering with Kalman filters and smoothers
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Abstract In this paper we present a documentation for an optimal filtering toolbox for the mathematical software package Matlab. The toolbox features many filtering methods for discrete-time state space models, including the well-known linear Kalman filter and

Predicting urban arterial travel time with state-space neural networks and Kalman filters
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A hybrid model for predicting urban arterial travel time on the basis of so-called state-space neural networks (SSNNs) and the extended Kalman filter (EKF) is presented. Previous research demonstrated that SSNNs can address complex nonlinear spatiotemporal

Attitude estimation by multiple-mode Kalman filters
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Abstract This paper is concerned with one dimensional attitude estimation using low cost accelerometer and gyroscope. To estimate attitude combining the two sensors, a multiple mode Kalman filter is proposed. Nonlinearity and time-varying parameters are partitioned

Beyond the Kalman filter: Particle filters for tracking applications
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Fort Adams is a one of a kind venue where you can host your wedding, even so beyond the kalman filter particle filters for tracking applications party, finally particle or private event even so applications. Its identity is designed on the even though beyond the kalman filter particle

Extended Kalman filters in the control structure of two-mass drive system
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Abstract. The paper deals with the application of the extended Kalman filters in the control structure of a two-mass drive system. In the first step only linear extended Kalman filter was used for the estimation of mechanical state variables of the drive including load torque

Switching kalman filters for prediction and tracking in an adaptive meteorological sensing network.
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ABSTRACT We consider the problem of configuring sen-sors in an adaptive sensor network being used to monitor meteorological features. One way to decide future sensor configurations is to base them on information currently being collected. For instance, if a

Application of a bank of Kalman filters and a robust Kalman filter for aircraft engine sensor/actuator fault diagnosis
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Abstract. In this paper, A Robust Kalman filter and a bank of Kalman filters are applied in fault detection and isolation (FDI) of sensor and actuator for aircraft gas turbine engine. A bank of Kalman filters is used to detect and isolate sensor fault, each of Kalman filter is

Analysis of dynamic sensor coverage problem using Kalman filters for estimation
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ABSTRACT We introduce a theoretical framework for the dynamic sensor coverage problem for the case with multiple discrete time linear stochastic systems placed at spacially separate locations. The objective is to keep an appreciable estimate of the states of the systems at

Comparison of neural networks and Kalman filters performances for fouling detection in a heat exchanger
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S Lalot, OP Palsson, GR Jonsson Journal of Heat 2007 programme-sfgp2011.eu ABSTRACT This paper presents the comparison between a neural network model and a Kalman filter model when applied for fouling detection. It monitored how the difference between estimated values and actual values evolve with time. This evolution is due to

A comparison of the extended and unscented Kalman filters for discrete-time systems with nondifferentiable dynamics
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Kalman filter, the unscented Kalman filter, and two extensions of the H8 filter when applied to discrete-time nonlinear state estimation problems with nondifferentiable dynamics. We compare the performance of all the estimation techniques on simple nonlinear examples

Real-time short-term traffic speed level forecasting and uncertainty quantification using layeredKalman filters
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Short-term traffic condition forecasting has long been argued as essential for developing proactive traffic control systems that could alleviate the growing congestion in the United States. In this field, short-term traffic condition level forecasting and short-term traffic

Data-model synchronization in extended Kalman filters for accurate online traffic state estimation
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T Schreiter, C Van Hinsbergen TFTC Summer 2010 infoscience.epfl.ch ABSTRACT Real-time freeway traffic state estimation plays an important role in Dynamic Traffic Management (DTM) and Advanced Traveler Information Systems (ATIS). One of the model- driven estimation techniques used in practice is based on the Extended Kalman Filter (

Fusion of odometry with magnetic sensors using Kalman filters and augmented system models for mobile robot navigation
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ABSTRACT This paper presents a comparative study of two data fusion methods for high precision mobile robot's pose estimation. Odometric data, provided by wheels encoders, are fused with data from magnetic markers detection. One of the methods uses an extended

Speech enhancement in temporal DFT trajectories using Kalman filters.
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ABSTRACT In this paper a time-frequency estimator for enhancement of noisy speech signals in the DFT domain is introduced. This estimator is based on modelling and filtering the temporal trajectories of the DFT components of noisy speech signal using Kalman filters.

Landsat tm satellite image restoration using kalman filters
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The quality of satellite images propagating through the atmosphere is affected by phenomena such as scattering and absorption of light, and turbulence, which degrade the image by blurring it and reducing its contrast. The atmospheric Wiener filter, which

Data-driven Kalman filters for non-uniformly sampled multirate systems with application to fault diagnosis
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W Li, S Shah PROCEEDINGS OF THE AMERICAN CONTROL 2005 eche.ualberta.ca ABSTRACT This paper first develops data-driven Kalman filters for non-uniformly sampled multirate systems. Then a novel methodology of fault detection and isolation for such systems is proposed. The proposed scheme is applied to a pilot scale experimental plant,

Using pseudo Kalman-filters in the presence of constraints application to sensing behaviors
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T Vi?ville, P Sander 1992 hal.archives-ouvertes.fr ABSTRACT Une nouvelle geeneeralisation du formalisme du Filtre de Kalman Lineeaire est introduite dans le cas de d'eequations non-lineeaires. L'interpreetation deeterministe, de ce meecanisme est discuteee. L'algorithme propos ee est une alternative au Filtre de Kalman

Structure from Motion via Two-State Pipeline of Extended Kalman Filters.
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ABSTRACT We introduce a novel approach to on-line structure from motion, using a pipelined pair of extended Kalman filters to improve accuracy with a minimal increase in computational cost. The two filters, a leading and a following filter, run concurrently on the

Using low-rank ensemble Kalman filters for data assimilation with high dimensional imperfect models
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ABSTRACT Low-rank square-root Kalman filters were developed for the efficient estimation of the state of high dimensional dynamical systems. These filters avoid the huge computational burden of the Kalman filter by approximating the filter's error covariance matrices by low-

Design of a chaos-based spread-spectrum communication system using dual UnscentedKalman Filters
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ABSTRACT It has been demonstrated recently than use of chaotic spreading codes can significantly increase transmission privacy for direct-sequence spread spectrum systems. In this note, we consider the problem of receiver synchronization as a dual estimation of the

State reconstructors: a possible alternative to asymptotic observers and Kalman filters
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ABSTRACT In this communication, a new algebraic approach is proposed for non-dynamic based, nonasymptotic, state estimation in linear systems. The state estimates are devised to be robust with respect to classical perturbation inputs to the system (ie, constant, ramp,

Designing Kalman Filters for Integration of Inertial Navigation System and Global Positioning System
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ABSTRACT Due to the strong growth of MEMS technology, the Inertial Navigation System (INS) is widely applied to navigation and guidance of aircraft movements. However, there are existing errors in the accelerometer and gyroscope signals that cause unacceptable drifts.

Error reduction for GPS accurate timing in power systems using Kalman filters and neural networks
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ABSTRACT. The Global Positioning System (GPS) based time reference provides inexpensive but highly-accurate timing and synchronization capability and meets requirements in power system fault location, monitoring, and control. Precision satellite clocks and time

Cubature Kalman Filters
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1 Cubature Kalman Filters Simon Haykin McMaster, University Hamilton, Ontario, Canada estimation tracking problems. Cubature Kalman filters provide the closest approximation to a Bayesian filter, which is optimal (the best we can do), at least in a conceptual sense.

Novel MEMS INS/GPS Integration Scheme Using Parallel Kalman Filters
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Abs!'rac1 ' In this paper, the special estimation schemes for INSlGPS integration is addressed and implemented on real-time PC-box hardware. The Strapdown INS [SINS) using two Kalman Filters (KP) has been built so that the system can be operated flexibly

Beyond the Kalman Filter: Particle filters for tracking applications
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ABSTRACT Target tracking is an important element of surveillance, guidance or obstacle avoidance, whose role is to determine the number, position and movement of targets. The fundamental building block of a tracking system is a filter for recursive state estimation.

Kalman filters for non-uniformly sampled multirate systems
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ABSTRACT This paper proposes Kalman filter algorithms, including one-step prediction and filtering, for non-uniformly sampled multirate systems. The stability and convergence of the algorithms are analyzed, and their application to fault detection as well as state estimation

Tracking of geoacoustic parameters using Kalman and particle filters
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Geoacoustic inversion is a technique used to extract information about the ocean environment by analyzing the acoustical field propagation in that medium. Typically, water column and seabed parameters such as sound speed profiles (SSPs), sediment densities,

Estimating changes in trend growth of total factor productivity: Kalman and HP filters versus a Markov-switching framework
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MW French 2001 federalreserve.gov ABSTRACT Trend growth in total factor productivity (TFP) is unobserved; it is frequently assumed to evolve continuously over time. That assumption is inherent in the use of the Hodrick-Prescott or Bandpass filter to extract trend. Similarly, the Kalman filter/unobserved


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