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2013 - Industrial & Engineering Chemistry Research
How To NOT Make the Extended Kalman Filter Fail

In an effort to assess the performance of newer estimation algorithms, many prior publications have presented comparative studies where the Extended Kalman Filter (EKF) failed. This is because the ...

1983 - Annual of Navigation
Kalman Filter Formulations for Transfer Alignment of Strapdown Inertial Units

Formulations of Kalman filters are presented which are capable of aligning one strapdown inertial sensor assembly with another by estimating the misalignment angle between them. One formulation treats the case of a fixed misalignment. Another treats the case of a dynamic misalignment, caused, say, by bending of the common supporting body. Measurements can be made by gyros only, or by gyros plus accelerometers. Filters which estimate inertial sensor error parameters are also discussed.

2013 - Aerospace Science and Technology
Robust Adaptive Kalman Filter for estimation of UAV dynamics in the presence of sensor/actuator faults

In this paper a Robust Adaptive Kalman Filter (RAKF) is introduced. The RAKF incorporates measurement and process noise covariance adaptation procedures (R and Q adaptation respectively) and utilizes adaptive factors in order to adapt itself against sensor/actuator faults. Thus the filter stands robust against the faults and even in case of sensor/actuator failure keeps providing accurate estimation results. In a single algorithm, the RAKF detects the fault, isolates it and applies the required adaptation process such that the estimation characteristic is not deteriorated. The performance of the proposed RAKF is investigated by simulations for the state estimation procedure of an Unmanned Aerial Vehicle.

1997 - IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society
Predictive head movement tracking using a Kalman filter

The use of head movements in control applications leaves the hands free for other tasks and utilizes the mobility of the head to acquire and track targets over a wide field of view. We present the results of applying a Kalman filter to generate prediction estimates for tracking head positions. A simple kinematics approach based on the assumption of a piecewise constant acceleration process is suggested and is shown to track head positions with an rms error under 2 degrees for head movements with accelerations smaller than 3000 degrees /s. To account for the wide range of head dynamic characteristics, an adaptive approach with input estimation is developed. The performance of the Kalman filter is compared to that based on a simple polynomial predictor.

1999 - IEEE Trans. Autom. Control.
Optimal solution of the two-stage Kalman estimator

The two-stage Kalman estimator was originally proposed to reduce the computational complexity of the augmented state Kalman filter. It was also applied to the tracking of maneuvering targets by treating the target acceleration as a bias term. Except in certain restrictive conditions, the conventional two-stage estimators are suboptimal in the sense that they are not equivalent to the augmented state filter. In this paper, the authors propose a new two-stage Kalman estimator, i.e., new structure, which is an extension of Friedland's estimator and is optimal in general conditions. In addition, we provide some analytic results to demonstrate the computational advantages of two-stage estimators over augmented ones.

2011 - NeuroImage
Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering

This paper presents a new approach to inverting (fitting) models of coupled dynamical systems based on state-of-the-art (cubature) Kalman filtering. Crucially, this inversion furnishes posterior estimates of both the hidden states and parameters of a system, including any unknown exogenous input. Because the underlying generative model is formulated in continuous time (with a discrete observation process) it can be applied to a wide variety of models specified with either ordinary or stochastic differential equations. These are an important class of models that are particularly appropriate for biological time-series, where the underlying system is specified in terms of kinetics or dynamics (i.e., dynamic causal models). We provide comparative evaluations with generalized Bayesian filtering (dynamic expectation maximization) and demonstrate marked improvements in accuracy and computational efficiency. We compare the schemes using a series of difficult (nonlinear) toy examples and conclude with a special focus on hemodynamic models of evoked brain responses in fMRI. Our scheme promises to provide a significant advance in characterizing the functional architectures of distributed neuronal systems, even in the absence of known exogenous (experimental) input; e.g., resting state fMRI studies and spontaneous fluctuations in electrophysiological studies. Importantly, unlike current Bayesian filters (e.g. DEM), our scheme provides estimates of time-varying parameters, which we will exploit in future work on the adaptation and enabling of connections in the brain.

1999 - Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C)
Circumventing dynamic modeling: evaluation of the error-state Kalman filter applied to mobile robot localization

The mobile robot localization problem is treated as a two-stage iterative estimation process. The attitude is estimated first and is then available for position estimation. The indirect (error state) form of the Kalman filter is developed for attitude estimation when applying gyro modeling. The main benefit of this choice is that combined dynamic modeling of the mobile robot and its interaction with the environment is avoided. The filter optimally combines the attitude rate information from the gyro and the absolute orientation measurements. The proposed implementation is independent of the structure of the vehicle or the morphology of the ground. The method can easily be transferred to another mobile platform provided it carries an equivalent set of sensors. The 2D case is studied in detail first. Results of extending the approach to the 3D case are presented. In both cases the results demonstrate the efficacy of the proposed method.

2005 - Monthly Weather Review
A Comparison between the 4DVAR and the Ensemble Kalman Filter Techniques for Radar Data Assimilation

Abstract A four-dimensional variational data assimilation (4DVAR) algorithm is compared to an ensemble Kalman filter (EnKF) for the assimilation of radar data at the convective scale. Using a cloud-resolving model, simulated, imperfect radar observations of a supercell storm are assimilated under the assumption of a perfect forecast model. Overall, both assimilation schemes perform well and are able to recover the supercell with comparable accuracy, given radial-velocity and reflectivity observations where rain was present. 4DVAR produces generally better analyses than the EnKF given observations limited to a period of 10 min (or three volume scans), particularly for the wind components. In contrast, the EnKF typically produces better analyses than 4DVAR after several assimilation cycles, especially for model variables not functionally related to the observations. The advantages of the EnKF in later cycles arise at least in part from the fact that the 4DVAR scheme implemented here does not use a forecast ...

2012 - Autom.
Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements

In this paper, the extended Kalman filtering problem is investigated for a class of nonlinear systems with multiple missing measurements over a finite horizon. Both deterministic and stochastic nonlinearities are included in the system model, where the stochastic nonlinearities are described by statistical means that could reflect the multiplicative stochastic disturbances. The phenomenon of measurement missing occurs in a random way and the missing probability for each sensor is governed by an individual random variable satisfying a certain probability distribution over the interval [0,1]. Such a probability distribution is allowed to be any commonly used distribution over the interval [0,1] with known conditional probability. The aim of the addressed filtering problem is to design a filter such that, in the presence of both the stochastic nonlinearities and multiple missing measurements, there exists an upper bound for the filtering error covariance. Subsequently, such an upper bound is minimized by properly designing the filter gain at each sampling instant. It is shown that the desired filter can be obtained in terms of the solutions to two Riccati-like difference equations that are of a form suitable for recursive computation in online applications. An illustrative example is given to demonstrate the effectiveness of the proposed filter design scheme.

2005 - Ocean Modelling
Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter

Abstract We describe the development of an efficient method for parameter estimation and ensemble forecasting in climate modelling. The technique is based on the ensemble Kalman filter and is several orders of magnitude more efficient than many others which have been previously used to address this problem. As well as being theoretically (near-)optimal, the method does not suffer from the `curse of dimensionality' and can comfortably handle multivariate parameter estimation. We demonstrate the potential of this method in identical twin testing with an intermediate complexity coupled AOGCM. The model's climatology is successfully tuned via the simultaneous estimation of 12 parameters. Several minor modifications arc described by which the method was adapted to a steady state (temporally averaged) case. The method is relatively simple to implement, and with only O(50) model runs required, we believe that optimal parameter estimation is now accessible even to computationally demanding models.

2013 - IEEE Transactions on Industrial Electronics
Industrial Applications of the Kalman Filter: A Review

The Kalman filter (KF) has received a huge interest from the industrial electronics community and has played a key role in many engineering fields since the 1970s, ranging, without being exhaustive, trajectory estimation, state and parameter estimation for control or diagnosis, data merging, signal processing, and so on. This paper provides a brief overview of the industrial applications and implementation issues of the KF in six topics of the industrial electronics community, highlighting some relevant reference papers and giving future research trends.

1999 - IEEE Trans. Robotics Autom.
Development and experimental validation of an adaptive extended Kalman filter for the localization of mobile robots

A basic requirement for an autonomous mobile robot is its capability to elaborate the sensor measures to localize itself with respect to a coordinate system. To this purpose, the data provided by odometric and sonar sensors are here fused together by means of an extended Kalman filter. The performance of the filter is improved by an online adjustment of the input and measurement noise covariances obtained by a suitably defined estimation algorithm.

1987 - Journal of Chemometrics
Kalman filtering approaches for solving problems in analytical chemistry

The application of the Kalman filter to the solution of a variety of problems in analytical chemistry is reviewed. Five examples are selected from the literature to illustrate the use of Kalman filtering techniques for obtaining least‐squares estimates fo several parameters of analytical importance. These examples include multicomponent curve resolution and concentration estimation, correction for variable background responses, calibration with drift compensation, and estimation of kinetic parameters for first‐order reactions and for heterogeneous charge‐transfer reactions. An adaptive Kalman filtering technique is required for the solution of the background correction problem, and the extended Kalman filter algorithm is required for the solution of the nonlinear kinetic problems. For each case, the results that were obtained are summarized, and some advantages of Kalman filtering over traditional least‐squares approaches are discussed.

2005 - EURASIP Journal on Wireless Communications and Networking
Maintaining Differentiated Coverage in Heterogeneous Sensor Networks

Most existing research considers homogeneous sensor networks, which suffer from performance bottleneck and poor scalability. In this paper, we adopt a heterogeneous sensor network model to overcome these problems. Sensing coverage is a fundamental problem in sensor networks and has been well studied over the past years. However, most coverage algorithms only consider the uniform coverage problem, that is, all the areas have the same coverage degree requirement. In many scenarios, some key areas need high coverage degree while other areas only need low coverage degree. We propose a differentiated coverage algorithm which can provide different coverage degrees for different areas. The algorithm is energy efficient since it only keeps minimum number of sensors to work. The performance of the differentiated coverage algorithm is evaluated through extensive simulation experiments. Our results show that the algorithm performs much better than any other differentiated coverage algorithm.

1972 - Transportation Science
Application of Kalman Filtering to the Surveillance and Control of Traffic Systems

The methodology of the discrete-time, extended Kalman filter is applied for the estimation of densities and the control of critical traffic links. The methodology is tested using traffic data obtained at the Lincoln tunnel of New York City. Two algorithms are tested, one involving density estimation alone and one combining density estimation with a formalism for the determination of optimal control. The results indicate that the first algorithm gives very good density estimates. The second algorithm yields a less accurate density estimate, but has the advantage over the first that it is amenable to an analytical optimization investigation.

2003 - Proceedings Ninth IEEE International Conference on Computer Vision
Segmenting foreground objects from a dynamic textured background via a robust Kalman filter

The algorithm presented aims to segment the foreground objects in video (e.g., people) given time-varying, textured backgrounds. Examples of time-varying backgrounds include waves on water, clouds moving, trees waving in the wind, automobile traffic, moving crowds, escalators, etc. We have developed a novel foreground-background segmentation algorithm that explicitly accounts for the nonstationary nature and clutter-like appearance of many dynamic textures. The dynamic texture is modeled by an autoregressive moving average model (ARMA). A robust Kalman filter algorithm iteratively estimates the intrinsic appearance of the dynamic texture, as well as the regions of the foreground objects. Preliminary experiments with this method have demonstrated promising results.

2001 - IEEE Trans. Signal Process.
Finite-horizon robust Kalman filter design

We study the problem of finite-horizon Kalman filtering for systems involving a norm-bounded uncertain block. A new technique is presented for robust Kalman filter design. This technique involves using multiple scaling parameters that ran be optimized by solving a semidefinite program. The use of optimized scaling parameters leads to an improved design. A recursive design method that can be applied to real-time applications is also proposed.

2003 - Journal of Global Positioning Systems
Adaptive Kalman Filtering for Vehicle Navigation

Kalman filters have been widely used for navigation and system integration. One of the key problems associated with Kalman filters is how to assign suitable statistical properties to both the dynamic and the observational models. For GPS navigation, the manoeuvre of the vehicle and the level of measurement noise are environmental dependent, and hardly to be predicted. Therefore to assign constant noise levels for such applications is not realistic. In this paper, real-time adaptive algorithms are applied to GPS data processing. Two different adaptive algorithms are discussed in the paper. A number of tests have been carried out to compare the performance of the adaptive algorithms with a conventional Kalman filter for vehicle navigation. The test results demonstrate that the new adaptive algorithms are much robust to the sudden changes of vehicle motion and measurement errors.

1977 - IEEE Trans. Inf. Theory
Kalman filtering in two dimensions

The Kalman filtering method is extended to two dimensions. The resulting computational load is found to be excessive. Two new approximations are then introduced. One, called the strip processor, updates a line segment at a time; the other, called the reduced update Kalman filter, is a scalar processor. The reduced update Kalman filter is shown to be optimum in that it minimizes the post update mean-square error (mse) under the constraint of updating only the nearby previously processed neighbors. The resulting filter is a general two-dimensional recursive filter.

2004 - IEEE Transactions on Automatic Control
Kalman filtering with intermittent observations

Motivated by navigation and tracking applications within sensor networks, we consider the problem of performing Kalman filtering with intermittent observations. When data travel along unreliable communication channels in a large, wireless, multihop sensor network, the effect of communication delays and loss of information in the control loop cannot be neglected. We address this problem starting from the discrete Kalman filtering formulation, and modeling the arrival of the observation as a random process. We study the statistical convergence properties of the estimation error covariance, showing the existence of a critical value for the arrival rate of the observations, beyond which a transition to an unbounded state error covariance occurs. We also give upper and lower bounds on this expected state error covariance.

Decentralized robust Kalman filtering for uncertain stochastic systems over heterogeneous sensor networks

Fuwen Yang
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Mahbub Gani
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Adrees Ahmad
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Fuwen Yang
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M. Gani
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Adrees Ahmad

Abstract:This paper investigates the problem of designing decentralized robust Kalman filters for sensor networks observing a physical process with parametric uncertainty. A sensor network consists of distributed collection of nodes, each of which has sensing, communication and computation capabilities. We consider a heterogeneous sensor network consisting of two types of nodes (type A and type B) and central base station. Type A nodes undertake the sensing and make noisy observations of the same physical process while type B nodes play the role of cluster-heads. We derive the information form of robust Kalman filter by using the Krein space approach which proves to be useful to fuse the cluster state estimates. We obtain the decentralized robust Kalman filter for each type B node for the state estimation of uncertain stochastic system by taking into consideration the sensing model of each cluster and the information form of robust Kalman filter. The type B nodes transmit their state estimates along with the inverse of error covariance matrix to the central base station which fuses the cluster state estimates to generate the overall global state estimate. Simulation results demonstrate that the performance of the centralized state estimate is comparable to the performance of the global state estimate and this suggests that they are identical.

参考文献

[1]  Ian R. Petersen,et al.  Robust state estimation and model validation for discrete-time uncertain systems with a deterministic description of noise and uncertainty , 1998, Autom..

[2]  Petros G. Voulgaris,et al.  On optimal ℓ∞ to ℓ∞ filtering , 1995, Autom..

[3]  Jin-Bae Park,et al.  Robust Kalman filtering via Krein space estimation , 2004 .

[4]  Sumit Roy,et al.  Decentralized structures for parallel Kalman filtering , 1988 .

[5]  M. F. Hassan,et al.  A decentralized computational algorithm for the global Kalman filter , 1978 .

[6]  Y. Bar-Shalom.  On the track-to-track correlation problem , 1981 .

[7]  François Baccelli,et al.  Stochastic geometry and architecture of communication networks , 1997, Telecommun. Syst..

[8]  Garry A. Einicke,et al.  Robust extended Kalman filtering , 1999, IEEE Trans. Signal Process..

[9]  Johannes Schumacher,et al.  Proceedings of the European Control Conference , 1997 .

[10]  Edward J. Coyle,et al.  An energy efficient hierarchical clustering algorithm for wireless sensor networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[11]  Qiang Du,et al.  Probabilistic methods for centroidal Voronoi tessellations and their parallel implementations , 2002, Parallel Comput..

[12]  A. Willsky,et al.  Combining and updating of local estimates and regional maps along sets of one-dimensional tracks , 1982 .

[13]  R. Olfati-Saber,et al.  Distributed Kalman Filter with Embedded Consensus Filters , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[14]  Mani Srivastava,et al.  Overview of sensor networks , 2004 .

[15]  C. Chang,et al.  Kalman filter algorithms for a multi-sensor system , 1976, 1976 IEEE Conference on Decision and Control including the 15th Symposium on Adaptive Processes.

[16]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[17]  Tian Zhi,et al.  Performance Evaluation of Track Fusion with Information , 2002 .

[18]  Jason Speyer,et al.  Computation and transmission requirements for a decentralized linear-quadratic-Gaussian control problem , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[19]  Mahbub Gani,et al.  Optimal Deployment Control for a Heterogeneous Mobile Sensor Network , 2006, 2006 9th International Conference on Control, Automation, Robotics and Vision.

[20]  Michael Athans,et al.  Proceedings of the MIT/ONR Workshop on Distributed Communication and Decision Problems Motivated by Naval C3 Systems (2nd). Held in Monterey, California on 16-27 July 1979. Volume 2 , 1980 .

[21]  H.F. Durrant-Whyte,et al.  Distributed And Decentralized Estimation , 1992, Singapore International Conference on Intelligent Control and Instrumentation [Proceedings 1992].

[22]  John E. Hershey,et al.  Computation , 1991, Digit. Signal Process..

[23]  Chee-Yee Chong,et al.  Distributed Tracking in Distributed Sensor Networks , 1986 .

[24]  Deborah Estrin,et al.  Guest Editors' Introduction: Overview of Sensor Networks , 2004, Computer.

[25]  Ian R. Petersen,et al.  Robust Kalman Filtering for Signals and Systems with Large Uncertainties , 1999 .

[26]  Lihua Xie,et al.  Robust Kalman filtering for uncertain discrete-time systems , 1994, IEEE Trans. Autom. Control..

[27]  Yaakov Bar-Shalom,et al.  The Effect of the Common Process Noise on the Two-Sensor Fused-Track Covariance , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[28]  Ossama Younis,et al.  Node clustering in wireless sensor networks: recent developments and deployment challenges , 2006, IEEE Network.

[29]  Catherine Rosenberg,et al.  A minimum cost heterogeneous sensor network with a lifetime constraint , 2005, IEEE Transactions on Mobile Computing.

[30]  Ilya V. Kolmanovsky,et al.  Predictive energy management of a power-split hybrid electric vehicle , 2009, 2009 American Control Conference.

[31]  Atsuyuki Okabe,et al.  Spatial Tessellations: Concepts and Applications of Voronoi Diagrams , 1992, Wiley Series in Probability and Mathematical Statistics.

[32]  Duncan McFarlane,et al.  Robust state estimation for uncertain systems , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[33]  Babak Hassibi,et al.  Indefinite-Quadratic Estimation And Control , 1987 .

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