Showing papers on "Kalman filter published in 1998"

C ONDENSATION —Conditional Density Propagation forVisual Tracking

Abstract: The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimo dal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together with visual observations, to propagate the random set over time. The result is highly robust tracking of agile motion. Notwithstanding the use of stochastic methods, the algorithm runs in near real-time.

Posterior Cramer-Rao bounds for discrete-time nonlinear filtering

Abstract: A mean-square error lower bound for the discrete-time nonlinear filtering problem is derived based on the van Trees (1968) (posterior) version of the Cramer-Rao inequality. This lower bound is applicable to multidimensional nonlinear, possibly non-Gaussian, dynamical systems and is more general than the previous bounds in the literature. The case of singular conditional distribution of the one-step-ahead state vector given the present state is considered. The bound is evaluated for three important examples: the recursive estimation of slowly varying parameters of an autoregressive process, tracking a slowly varying frequency of a single cisoid in noise, and tracking parameters of a sinusoidal frequency with sinusoidal phase modulation.

Decentralized estimation and control for multisensor systems

Abstract: Introduction Background Motivation Problem Statement Approach Principal Contributions Book Outline Estimation and Information Space Introduction The Kalman Filter The Information Filter The Extended Kalman Filter (EKF) The Extended Information Filter (EIF) Examples of Estimation in Nonlinear Systems Summary Decentralized Estimation for Multisensor Systems Introduction Multisensor Systems Decentralized Systems Decentralized Estimators The Limitations of Fully Connected Decentralization Summary Scalable Decentralized Estimation Introduction An Extended Example The Moore-Penrose Generalized Inverse: T+ Generalized Internodal Transformation Special Cases of Tji(k) Distributed and Decentralized Filters Summary Scalable Decentralized Control Introduction Optimal Stochastic Control Decentralized Multisensor Based Control Simulation Example Summary Multisensor Applications: A Wheeled Mobile Robot Introduction Wheeled Mobile Robot (WMR) Modeling Decentralized WMR Control Hardware Design and Construction Software Development On-Vehicle Software Summary Results and Performance Analysis Introduction System Performance Criteria Simulation Results WMR Experimental Results Summary Conclusions and Future Research Introduction Summary of Contributions Research Appraisal Future Research Directions Bibliography

Adaptive AR modeling of nonstationary time series by means of Kalman filtering

Abstract: An adaptive on-line procedure is presented for autoregressive (AR) modeling of nonstationary multivariate time series by means of Kalman filtering. The parameters of the estimated time-varying model can be used to calculate instantaneous measures of linear dependence. The usefulness of the procedures in the analysis of physiological signals is discussed in two examples: first, in the analysis of respiratory movement, heart rate fluctuation, and blood pressure, and second, in the analysis of multichannel electroencephalogram (EEG) signals. It was shown for the first time that in intact animals the transition from a normoxic to a hypoxic state requires tremendous short-term readjustment of the autonomic cardiac-respiratory control. An application with experimental EEG data supported observations that the development of coherences among cell assemblies of the brain is a basic element of associative learning or conditioning.

An experimental comparison of localization methods

Abstract: Localization is the process of updating the pose of a robot in an environment, based on sensor readings. In this experimental study, we compare two methods for localization of indoor mobile robots: Markov localization, which uses a probability distribution across a grid of robot poses; and scan matching, which uses Kalman filtering techniques based on matching sensor scans. Both these techniques are dense matching methods, that is, they match dense sets of environment features to an a priori map. To arrive at results for a range of situations, we utilize several different types of environments, and add noise to both the dead-reckoning and the sensors. Analysis shows that, roughly, the scan-matching techniques are more efficient and accurate, but Markov localization is better able to cope with large amounts of noise. These results suggest hybrid methods that are efficient, accurate and robust to noise.

Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters

Abstract: Detection and tracking algorithms of the blood vessel network in the retinal images are proposed. Two main groups of algorithms are employed for this task, i.e., scanning and tracking. According to the known blood vessel feature, a second-order derivative Gaussian matched filter is designed and used to locate the center point and width of a vessel in its cross sectional profile. Together with this the extended Kalman filter is employed for the optimal linear estimation of the next possible location of blood vessel segment by appropriate formulation of its pattern changing process and observation model. To check the bifurcation in the vessel network, a simple branching detection strategy is implemented during tracking. The proposed algorithms all work well in the whole tracking process and can detect more complete vessel network in the ocular fundus photographs.

Incorporation of time delayed measurements in a discrete-time Kalman filter

Abstract: In many practical systems there is a delay in some of the sensor devices, for instance vision measurements that may have a long processing time. How to fuse these measurements in a Kalman filter is not a trivial problem if the computational delay is critical. Depending on how much time there is at hand, the designer has to make trade offs between optimality and computational burden of the filter. In this paper various methods in the literature along with a new method proposed by the authors will be presented and compared. The new method is based on "extrapolating" the measurement to present time using past and present estimates of the Kalman filter and calculating an optimal gain for this extrapolated measurement.

Special topics of gesture recognition applied in intelligent home environments

Abstract: This report shows how to realize a gesture recognition system for controlling appliances in home environments. It gives a brief overview on an existing system and clarifies details on ergonomic remote control of devices by gestures with the help of a vision system. The focus is on the motion detection, object normalization and identification, the modelling and the prediction of motion by the Kalman Filter. A main interest was to show through the example ARGUS, how the Kalman Filter should be modelled and initialized for a physical human motion. The initialization problem of the Kalman Filter of a vision based system for human motion tracking differs from initializing for physical systems, where manuals report measurement errors. Most aspects mentioned in this report were implemented in the ARGUS prototype.

The Kriged Kalman filter

Abstract: In recent years there has been growing interest in spatial-temporal modelling, partly due to the potential of large scale data in pollution and global climate monitoring to answer important environmental questions. We consider the Kriged Kalman filter (KKF), a powerful modelling strategy which combines the two wellestablished approaches of (a) Kriging, in the field of spatial statistics, and (b) the Kalman filter, in general state space formulations of multivariate time series analysis. We give a brief introduction to the model and describe its various properties, and highlight that the model allows prediction in time as well as in space, simultaneously. Some special cases of the time series model are considered. We give some procedures to implement the model, also illustrated through a practical example. The paper concludes with a discussion.

Estimation of Angular Velocity and Acceleration from Shaft-Encoder Measurements

Abstract: This paper suggests a Kalman-filter approach to the estimation of angular velocity and acceleration from (quantized) shaft-encoder measurements Finite-difference estimates deteriorate as sampling rates are increased For small sampling periods, we show that the filtering problem is the dual of the cheap control problem, and we jus tify the use of all-integrator models We investigate Kalman filtering with constant sampling rate, and also with measurements triggered by encoder pulses Simulation and experimental results are given

A Kalman filter approach to track fast impedance changes in electrical impedance tomography

Abstract: In electrical impedance tomography (EIT), an estimate for the cross-sectional impedance distribution is obtained from the body by using current and voltage measurements made from the boundary. All well-known reconstruction algorithms use a full set of independent current patterns for each reconstruction. In some applications, the impedance changes may be so fast that information on the time evolution of the impedance distribution is either lost or severely blurred. Here, the authors propose an algorithm for EIT reconstruction that is able to track fast changes in the impedance distribution. The method is based on the formulation of EIT as a state-estimation problem and the recursive estimation of the state with the aid of the Kalman filter. The performance of the proposed method is evaluated with a simulation of human thorax in a situation in which the impedances of the ventricles change rapidly. The authors show that with optimal current patterns and proper parameterization, the proposed approach yields significant enhancement of the temporal resolution over the conventional reconstruction strategy.

Nonlinear modeling : advanced black-box techniques

Abstract: Preface. 1. Neural Nets and Related Model Structures for Nonlinear System Identification J. Sjoberg, L.S.H. Ngia. 2. Enhanced Multi-Stream Kalman Filter Training for Recurrent Networks L.A. Feldkamp, et al. 3. The Support Vector Method of Function Estimation V. Vapnik. 4. Parametric Density Estimation for the Classification of Acoustic Feature Vectors in Speech Recognition S. Basu, C.A. Micchelli. 5. Wavelet Based Modeling of Nonlinear Systems Yi Yu, et al. 6. Nonlinear Identification Based on Fuzzy Models V. Wertz, S. Yurkovich. 7. Statistical Learning in Control and Matrix Theory M. Vidyasagar. 8. Nonlinear Time-Series Analysis U. Parlitz. 9. The K.U. Leuven Time Series Prediction Competition J.A.K. Suykens, J. Vandewalle. References. Index.

Sensor fault detection and identification in a mobile robot

Abstract: Multiple model adaptive estimation (MMAE) is used to detect and identify sensor failures in a mobile robot. Each estimator is a Kalman filter with a specific embedded failure model. The filter bank also contains one filter which has the nominal model embedded within it. The filter residuals are postprocessed to produce a probabilistic interpretation of the operation of the system. The output of the system at any given time is the confidence in the correctness of the various embedded models. As an additional feature the standard assumption that the measurements are available at a constant, common frequency, is relaxed. Measurements are assumed to be asynchronous and of varying frequency. The particularly difficult case of 'soft' sensor failure is also handled successfully. A system architecture is presented for the general problem of failure detection and identification in mobile robots. As an example, the MMAE algorithm is demonstrated on a Pioneer I robot in the case of three different sensor failures.

Dynamic models of human motion

Abstract: This paper describes experiments in human motion understanding, defined here as estimation of the physical state of the body (the Plant) combined with interpretation of that part of the motion that cannot be predicted by the plant alone (the Behavior). The described behavior system operates in conjunction with a real-time, fully-dynamic, 3-D person tracking system that provides a mathematically concise formulation for incorporating a wide variety of physical constraints and probabilistic influences. The framework takes the form of a non-linear recursive filter that enables pixel-level, probabilistic processes to take advantage of the contextual knowledge encoded in the higher-level models. Results are shown that demonstrate both qualitative and quantitative gains in tracking performance.

Robust Estimation and Failure Detection: A Concise Treatment

Abstract: From the Publisher: This work presents a concise treatment of robust estimation, with a thorough presentation of Kalman filtering. The robust game theoretic/H[subscript [infinity]] filtering theory is developed, making it possible to design estimators that are more general than Kalman filters and are robust to model uncertainties and rapid model variations. It also reviews the likelihood ratio method for failure detection and demonstrates how robust filters can enhance such methods by enabling the design of failure detectors that are sensitive to failures but insensitive to model uncertainties and/or rapid model variations. Robust Estimation and Failure Detection is of particular value to students, researchers and engineers with an interest in filtering or failure detection, offering classical and advanced theories and design methods and allowing them to benefit from the robust control theoretic developments of the last fifteen years. Control researchers and engineers will also find it relevant, as it demonstrates how development in their discipline affects these two neighbouring fields.

Optimal filtering for systems with unknown inputs

Abstract: An optimal filtering formula is derived for linear time-varying discrete systems with unknown inputs. By making use of the well-known innovations filtering technique, the derivation is an extension of a new observer design method for time-invariant deterministic systems with unknown inputs. The systems under consideration have the most general form. The derived optimal filter has a similar form to the standard Kalman filter with some modified covariance and gain matrices.

State space modeling of long-memory processes

Abstract: This paper develops a state space modeling for long-range dependent data. Although a long-range dependent process has an infinite-dimensional state space representation, it is shown that by using the Kalman filter, the exact likelihood function can be computed recursively in a finite number of steps. Furthermore, an approximation to the likelihood function based on the truncated state space equation is considered. Asymptotic properties of these approximate maximum likelihood estimates are established for a class of long-range dependent models, namely, the fractional autoregressive moving average models. Simulation studies show rapid converging properties of the approximate maximum likelihood approach.

A method for real-time nonlinear system state estimation and control

Abstract: A method for the estimation of the state variables of nonlinear systems with exogenous inputs is based on improved extended Kalman filtering (EKF) type techniques. The method uses a discrete-time model, based on a set of nonlinear differential equations describing the system, that is linearized about the current operating point. The time update for the state estimates is performed using integration methods. Integration, which is accomplished through the use of matrix exponential techniques, avoids the inaccuracies of approximate numerical integration techniques. The updated state estimates and corresponding covariance estimates use a common time-varying system model for ensuring stability of both estimates. Other improvements include the use of QR factorization for both time and measurement updating of square-root covariance and Kalman gain matrices and the use of simulated annealing for ensuring that globally optimal estimates are produced.

Maximum likelihood track-before-detect with fluctuating target amplitude

Abstract: An important problem in target tracking is the detection and tracking of targets in very low signal-to-noise ratio (SNR) environments. In the past, several approaches have been used, including maximum likelihood. The major novelty of this work is the incorporation of a model for fluctuating target amplitude into the maximum likelihood approach for tracking of constant velocity targets. Coupled with a realistic sensor model, this allows the exploitation of signal correlation between resolution cells in the same frame, and also from one frame to the next. The fluctuating amplitude model is a first order model to reflect the inter-frame correlation. The amplitude estimates are obtained using a Kalman filter, from which the likelihood function is derived. A numerical maximization technique avoids problems previously encountered in "velocity filtering" approaches due to mismatch between assumed and actual target velocity, at the cost of additional computation. The Cramer-Rao lower bound (CRLB) is derived for a constant, known amplitude case. Estimation errors are close to this CRLB even when the amplitude is unknown. Results show track detection performance for unknown signal amplitude is nearly the same as that obtained when the correct signal model is used.

Diagnosing Shocks in Time Series

Abstract: Efficient means of modeling aberrant behavior in times series are developed. Our methods are based on state-space forms and allow test statistics for various interventions to be computed from a single run of the Kalman filter smoother. The approach encompasses existing detection methodologies. Departures commonly observed in practice, such as outlying values, level shifts, and switches, are readily dealt with. New diagnostic statistics are proposed. Implications for structural models, autoregressive integrated moving average models, and models with explanatory variables are given.

Fast Filtering and Smoothing for Multivariate State Space Models

Abstract: This paper gives a new approach to diffuse filtering and smoothing for multivariate state space models. The standard approach treats the observations as vectors while our approach treats each element of the observational vector individually. This strategy leads to computationally efficient methods for multivariate filtering and smoothing. Also, the treatment of the diffuse initial state vector in multivariate models is much simpler than existing methods. The paper presents details of relevant algorithms for filtering, prediction and smoothing. Proofs are provided. Three examples of multivariate models in statistics and economics are presented for which the new approach is particularly relevant.

A signal processing framework based on dynamic neural networks with application to problems in adaptation, filtering, and classification

Abstract: We present a coherent neural net based framework for solving various signal processing problems. It relies on the assertion that time-lagged recurrent networks possess the necessary representational capabilities to act as universal approximators of nonlinear dynamical systems. This applies to system identification, time-series prediction, nonlinear filtering, adaptive filtering, and temporal pattern classification. We address the development of models of nonlinear dynamical systems, in the form of time-lagged recurrent neural nets, which can be used without further training. We employ a weight update procedure based on the extended Kalman filter (EKF). Against the tendency for a net to forget earlier learning as it processes new examples, we develop a technique called multistream training. We demonstrate our framework by applying it to 4 problems. First, we show that a single time-lagged recurrent net can be trained to produce excellent one-time-step predictions for two different time series and also to be robust to severe errors in the input sequence. Second, we model stably a complex system containing significant process noise. The remaining two problems are drawn from real-world automotive applications. One involves input-output modeling of the dynamic behavior of a catalyst-sensor system which is exposed to an operating engine's exhaust stream, the other the real-time and continuous detection of engine misfire.

Adaptive H^∞ filter: Its application to structural identification

Abstract: By adding the function of memory fading for past observation data to the \iH\d∞ filter, the adaptive \iH\d∞ filter was developed for identifying structural systems with nonstationary dynamic characteristics. Identification algorithms are proposed for time-varying structural systems in which the velocity and displacement of each floor are available for observation, as well as for the case when only the velocity and displacement of some floors are available. The Akaike-Bayes information criterion is used to determine the optimal forgetting factor. Identification algorithms that use the adaptive \iH\d∞ and Kalman filters are applied to a five-degree of freedom (DOF) linear system with nonstationary dynamic characteristics and to a five-DOF nonlinear structural system. Digital simulation results show that the adaptive \iH\d∞ filter efficiently traces the time-varying properties of structural systems. The behavior of the adaptive \iH\d∞ filter is better than that of the adaptive Kalman filter for identifying a structural system with time-varying dynamic characteristics. The former is more efficient and robust for identifying structural systems with nonstationary dynamic characteristics.

Failure detection and identification and fault tolerant control using the IMM-KF with applications to the Eagle-Eye UAV

Abstract: We describe a novel approach to sensor/actuator failure detection and identification and fault tolerant control based on the interacting multiple model (IMM) Kalman filter approach. Failures are mapped into different (and unique) state-space model representations. The IMM algorithm computes (online) the posterior probability of each failure model, that can be interpreted as a failure indicator. The fault tolerant control approach presented is based on a multiple model control law, where an optimal controller is designed for each actuator failure model, and the control action is a combination of the individual outputs of each controller weighted by the posterior probability associated with that model. The new FDI-FTC approach was tested on a linear simulation of Bell Helicopter's Eagle-Eye unmanned air vehicle. All single sensor and actuator failures were detected and properly identified, as well as some simultaneous failures.

Bearings-only target motion analysis based on a multihypothesis Kalman filter and adaptive ownship motion control

Abstract: The author presents a multihypothesis cartesian Kalman filter (MHCKF) applied to the problem of bearings-only target motion analysis (TMA) from a single moving platform. A method to adaptively control the ownship motion based on a measure of "available range information" extracted from the MHCKF is also presented. The properties of the MHCKF algorithm are discussed qualitatively and illustrated by examples. The adaptive ownship motion is demonstrated and the estimated range is compared with the results from a fixed, two-leg ownship trajectory.

Autonomous land vehicle navigation using millimeter wave radar

Abstract: This paper discusses the use of a 77 GHz millimeter wave radar as a guidance sensor for autonomous land vehicle navigation. A test vehicle has been fitted with a radar and encoders that give steer angle and velocity. An extended Kalman filter optimally fuses the radar range and bearing measurements with vehicle control signals to give estimated position and variance as the vehicle moves around a test site. The effectiveness of this data fusion is compared with encoders alone and with a satellite positioning system. Consecutive scans have been combined to give a radar image of the surrounding environment. Data in this format are invaluable for future work on collision detection and map building navigation.

Robust car tracking using Kalman filtering and Bayesian templates

Abstract: We present a real-time model-based vision approach for detecting and tracking vehicles from a moving platform. It was developed in the context of the CMU Navlab project and is intended to provide the Navlabs with situational awareness in mixed traffic. Tracking is done by combining a simple image processing techniques with a 3D extended Kalman filter and a measurement equation that projects from the 3D model to image space. No ground plane assumption is made. The resulting system runs at frame rate or higher, and produces excellent estimates of road curvature, distance to and relative speed of a tracked vehicle. We have complemented the tracker with a novel machine learning based algorithm for car detection, the CANSS algorithm, which serves to initialize tracking.