Showing papers on "Kalman filter published in 1996"

Contour Tracking by Stochastic Propagation of Conditional Density

Abstract: The problem of tracking curves in dense visual clutter is a challenging one. Trackers based on Kalman filters are of limited use; because they are based on Gaussian densities which are unimodal, they cannot represent simultaneous alternative hypotheses. Extensions to the Kalman filter to handle multiple data associations work satisfactorily in the simple case of point targets, but do not extend naturally to continuous curves. A new, stochastic algorithm is proposed here, the Condensation algorithm — Conditional Density Propagation over time. It uses ‘factored sampling’, a method previously applied to interpretation of static images, in which the distribution of possible interpretations is represented by a randomly generated set of representatives. The Condensation algorithm combines factored sampling with learned dynamical models to propagate an entire probability distribution for object position and shape, over time. The result is highly robust tracking of agile motion in clutter, markedly superior to what has previously been attainable from Kalman filtering. Notwithstanding the use of stochastic methods, the algorithm runs in near real-time.

An efficient implementation of Reid's multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking

Abstract: An efficient implementation of Reid's multiple hypothesis tracking (MHT) algorithm is presented in which the k-best hypotheses are determined in polynomial time using an algorithm due to Murly (1968). The MHT algorithm is then applied to several motion sequences. The MHT capabilities of track initiation, termination, and continuation are demonstrated together with the latter's capability to provide low level support of temporary occlusion of tracks. Between 50 and 150 corner features are simultaneously tracked in the image plane over a sequence of up to 51 frames. Each corner is tracked using a simple linear Kalman filter and any data association uncertainty is resolved by the MHT. Kalman filter parameter estimation is discussed, and experimental results show that the algorithm is robust to errors in the motion model. An investigation of the performance of the algorithm as a function of look-ahead (tree depth) indicates that high accuracy can be obtained for tree depths as shallow as three. Experimental results suggest that a real-time MHT solution to the motion correspondence problem is possible for certain classes of scenes.

Contour Tracking by Stochastic Propagation of Conditional Density.

Abstract: The problem of tracking curves in dense visual clutter is a challenging one. Trackers based on Kalman filters are of limited use; because they are based on Gaussian densities which are unimodal, they cannot represent simultaneous alternative hypotheses. Extensions to the Kalman filter to handle multiple data associations work satisfactorily in the simple case of point targets, but do not extend naturally to continuous curves. A new, stochastic algorithm is proposed here, the Condensation algorithm — Conditional Density Propagation over time. It uses ‘factored sampling’, a method previously applied to interpretation of static images, in which the distribution of possible interpretations is represented by a randomly generated set of representatives. The Condensation algorithm combines factored sampling with learned dynamical models to propagate an entire probability distribution for object position and shape, over time. The result is highly robust tracking of agile motion in clutter, markedly superior to what has previously been attainable from Kalman filtering. Notwithstanding the use of stochastic methods, the algorithm runs in near real-time.

Relative end-effector control using Cartesian position based visual servoing

Abstract: This paper presents a complete design methodology for Cartesian position based visual servo control for robots with a single camera mounted at the end-effector. Position based visual servo control requires the explicit calculation of the relative position and orientation (POSE) of the workpiece object with respect to the camera. This is accomplished using image plane measurements of a number of known feature points on the object, and then applying an extended Kalman filter to obtain a recursive solution of the photogrammetric equations, and to properly combine redundant measurements. The control is then designed by specifying the desired trajectories with respect to the object and forming the control error in the end-effector frame. The implementation using a distributed computer architecture is described. An experimental system has been built and used to evaluate the performance of the POSE estimation and the position based visual servo control. Several results for relative trajectory control and target tracking are presented. Results of the experiments showing the effect of loss of some of the redundant features are also presented.

Smoothness priors analysis of time series

Abstract: 1 Introduction.- 1.1 Background.- 1.2 What is in the Book.- 1.3 Time Series Examples.- 2 Modeling Concepts and Methods.- 2.1 Akaike's AIC: Evaluating Parametric Models.- 2.1.1 The Kullback-Leibler Measure and the Akaike AIC.- 2.1.2 Some Applications of the AIC.- 2.1.3 A Theoretical Development of the AIC.- 2.1.4 Further Discussion of the AIC.- 2.2 Least Squares Regression by Householder Transformation.- 2.3 Maximum Likelihood Estimation and an Optimization Algorithm.- 2.4 State Space Methods.- 3 The Smoothness Priors Concept.- 3.1 Introduction.- 3.2 Background, History and Related Work.- 3.3 Smoothness Priors Bayesian Modeling.- 4 Scalar Least Squares Modeling.- 4.1 Estimating a Trend.- 4.2 The Long AR Model.- 4.3 Transfer Function Estimation.- 4.3.1 Analysis.- 4.3.2 A Transfer Function Analysis Example.- 5 Linear Gaussian State Space Modeling.- 5.1 Introduction.- 5.2 Standard State Space Modeling.- 5.3 Some State Space Models.- 5.4 Modeling With Missing Observations.- 5.5 Unequally Spaced Observations.- 5.6 An Information Square-Root Filter/Smoother.- 6 Contents General State Space Modeling.- 6.1 Introduction.- 6.2 The General State Space Model.- 6.2.1 General Filtering and Smoothing.- 6.2.2 Model Identification.- 6.3 Numerical Synthesis of the Algorithms.- 6.4 The Gaussian Sum-Two Filter Formula Approximation.- 6.4.1 The Gaussian Sum Approximation.- 6.4.2 The Two-filter Formula and Gaussian Sum Smoothing.- 6.4.3 Remarks on the Gaussian Mixture Approximation.- 6.5 A Monte Carlo Filtering and Smoothing Method.- 6.5.1 Introduction.- 6.5.2 Non-Gaussian Nonlinear State Space Model and Filtering.- 6.5.3 Smoothing.- 6.6 A Derivation of the Kalman filter.- 6.6.1 Preparations.- 6.6.2 Derivation of the Filter and Smoother.- 7 Applications of Linear Gaussian State Space Modeling.- 7.1 AR Time Series Modeling.- 7.2 Kullback-Leibler Computations.- 7.3 Smoothing Unequally Spaced Data.- 7.4 A Signal Extraction Problem.- 7.4.1 Estimation of the Time Varying Variance.- 7.4.2 Separating a Micro Earthquake From Noisy Data.- 7.4.3 A Second Example.- 8 Modeling Trends.- 8.1 State Space Trend Models.- 8.2 State Space Estimation of Smooth Trend.- 8.2.1 Estimation of a Smooth Trend.- 8.2.2 Smooth Trend Plus Autoregressive Model.- 8.3 Multiple Time Series Modeling: The Common Trend Plus Individual Component AR Model.- 8.3.1 Maximum Daily Temperatures 1971-1992.- 8.3.2 Tiao and Tsay Flour Price Data.- 8.4 Modeling Trends with Discontinuities.- 8.4.1 Pearson Family, Gaussian Mixture and Monte Carlo Filter Es-timation of an Abruptly Changing Trend.- 9 Seasonal Adjustment.- 9.1 Introduction.- 9.2 A State Space Seasonal Adjustment Model.- 9.3 Smooth Seasonal Adjustment Examples.- 9.4 Non-Gaussian Seasonal Adjustment.- 9.5 Modeling Outliers.- 9.6 Legends.- 10 Estimation of Time Varying Variance.- 10.1 Introduction and Background.- 10.2 Modeling Time-Varying Variance.- 10.3 The Seismic Data.- 10.4 Smoothing the Periodogram.- 10.5 The Maximum Daily Temperature Data.- 11 Modeling Scalar Nonstationary Covariance Time Series.- 11.1 Introduction.- 11.2 A Time Varying AR Coefficient Model.- 11.3 A State Space Model.- 11.3.1 Instantaneous Spectral Density.- 11.4 PARCOR Time Varying AR Modeling.- 11.5 Examples.- 12 Modeling Multivariate Nonstationary Covariance Time Series.- 12.1 Introduction.- 12.2 The Instantaneous Response-Orthogonal Innovations Model.- 12.3 State Space Modeling.- 12.4 Time Varying PARCOR VAR Modeling.- 12.4.1 Constant Coefficient PARCOR VAR Time Series Modeling.- 12.4.2 Time Varying PARCOR Coefficient VAR Modeling.- 12.5 Examples.- 13 Modeling Inhomogeneous Discrete Processes.- 13.1 Nonstationary Discrete Process.- 13.2 Nonstationary Binary Processes.- 13.3 Nonstationary Poisson Process.- 14 Quasi-Periodic Process Modeling.- 14.1 The Quasi-periodic Model.- 14.2 The Wolfer Sunspot Data.- 14.3 The Canadian Lynx Data.- 14.4 Other Examples.- 14.4.1 Phase-unwrapping.- 14.4.2 Quasi-periodicity in the Rainfall data.- 14.5 Predictive Properties of Quasi-periodic Process Modeling.- 15 Nonlinear Smoothing.- 15.1 Introduction.- 15.2 State Estimation.- 15.3 A One Dimensional Problem.- 15.4 A Two Dimensional Problem.- 16 Other Applications.- 16.1 A Large Scale Decomposition Problem.- 16.1.1 Data Preparation and a Strategy for the Data Analysis.- 16.1.2 The Data Analysis.- 16.2 Markov State Classification.- 16.2.1 Introduction.- 16.2.2 A Markov Switching Model.- 16.2.3 Analysis and Results.- 16.3 SPVAR Modeling for Spectrum Estimation.- 16.3.1 Background.- 16.3.2 The Approach and an Example.- References.- Author Index.

Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter

Abstract: Current virtual environment and teleoperator applications are hampered by the need for an accurate, quick-responding head-tracking system with a large working volume. Gyroscopic orientation sensors can overcome problems with jitter, latency, interference, line-of-sight obscurations and limited range, but suffer from slow drift. Gravimetric inclinometers can detect attitude without drifting, but are slow and sensitive to transverse accelerations. This paper describes the design of a Kalman filter to integrate the data from these two types of sensors in order to achieve the excellent dynamic response of an inertial system without drift, and without the acceleration sensitivity of inclinometers.

Data assimilation and inverse methods in terms of a probabilistic formulation

Abstract: The weak constraint inverse for nonlinear dynamical models is discussed and derived in term of a probabilistic formulation. The well-known result that for Gaussian error statistics the minimum of the weak constraint inverse is equal to the maximum-likelihood estimate is rederived. Then several methods based on ensemble statistics that can be used to find the smoother (as opposed to the filter) solution are introduced and compared to traditional methods. A strong point of the new methods is that they avoid the integration of adjoint equations, which is a complex task for real oceanographic or atmospheric applications. They also avoid iterative searches in a Hilbert space, and error estimates can be obtained without much additional computational effort. The feasibility of the new methods is illustrated in a two-layer quasigeostrophic ocean model.

Assimilation of Geosat Altimeter Data for the Agulhas Current Using the Ensemble Kalman Filter with a Quasigeostrophic Model

Abstract: The ring-shedding process in the Agulhas Current is studied using the ensemble Kalman filter to assimilate Geosat altimeter data into a two-layer quasigeostrophic ocean model. The properties of the ensemble Kalman filter are further explored with focus on the analysis scheme and the use of gridded data. The Geosat data consist of 10 fields of gridded sea surface height anomalies separated 10 days apart that are added to a climatic mean field. This corresponds to a huge number of data values, and a data reduction scheme must be applied to increase the efficiency of the analysis procedure. Further, it is illustrated how one can resolve the rank problem occurring when a too large dataset or a small ensemble is used.

Introduction to random signals and applied Kalman filtering : with MATLAB exercises and solutions

Abstract: Probability and Random Variables: A Review. Mathematical Description of Random Signals. Response of Linear Systems to Random Inputs. Wiener Filtering. The Discrete Kalman Filter, State-Space Modeling, and Simulation. Prediction, Applications, and More Basics on Discrete Kalman Filtering. The Continuous Kalman Filter. Smoothing. Linearization and Additional Intermediate-Level Topics on Applied Kalman Filtering. More on Modeling: Integration of Noninertial Measurements Into INS. The Global Positioning System: A Case Study. Appendices. Index.

A moving horizon-based approach for least-squares estimation

Abstract: A general formulation of the moving horizon estimator is presented. An algorithm with a fixed-size estimation window and constraints on states, disturbances, and measurement noise is developed, and a probabilistic interpretation is given. The moving horizon formulation requires only one more tuning parameter (horizon size) than many well-known approximate nonlinear filters such as extended Kalman filter (EFK), iterated EKF, Gaussian second-order filter, and statistically linearized filter. The choice of horizon size allows the user to achieve a compromise between the better performance of the batch least-squares solution and the reduced computational requirements of the approximate nonlinear filters. Specific issues relevant to linear and nonlinear systems are discussed with comparisons made to the Kalman filter, EKF, and other recursive and optimization-based estimation schemes.

Robust discrete-time minimum-variance filtering

Abstract: The bounded-variance filtered estimation of the state of an uncertain, linear, discrete-time system, with an unknown norm-bounded parameter matrix, is considered. An upper bound on the variance of the estimation error is found for all admissible systems, and estimators are derived that minimize the latter bound. We treat the finite-horizon, time-varying case and the infinite-time case, where the nominal system model is time invariant. In the special stationary case, where it is known that the uncertain system is time invariant, we provide a robust filter for all uncertainties that still keep the system asymptotically stable.

Sliding Mode Control Using Modified Rodrigues Parameters

Abstract: Introduction Spacecraft pointing poses a complex problem involving nonlinear dynamics with either linear and/or nonlinear control laws. Primary control actuators usually include thrusters for rapid and coarse attitude maneuvers, and reaction wheels for slow and precise attitude maneuvers. Other types of control mechanisms include gravity-gradient stabilization and magnetic torquer assemblies. Control algorithms can be divided into open-loop systems and closed-loop (feedback) systems. Open-loop systems usually require a pre-determined pointing maneuver, and are typically determined using optimal control techniques which involve the solution of a twopoint-boundary-problem. An example of open-loop control is the time-optimal attitude maneuver (e.g., see the excellent survey paper by Scrivener and Thompson [1]). Closed-loop systems can provide robustness with respect to spacecraft modeling uncertainties and unexpected disturbances.

Mapping tropical Pacific sea level : Data assimilation via a reduced state space Kalman filter

Abstract: The well-known fact that tropical sea level can be usefully simulated by linear wind driven models recommends it as a realistic test problem for data assimilation schemes. Here we report on an assimilation of monthly data for the period 1975-1992 from 34 tropical Pacific tide gauges into such a model using a Kalman filter. We present an approach to the Kalman filter that uses a reduced state space representation for the required error covariance matrices. This reduction makes the calculation highly feasible. We argue that a more complete representation will be of no value in typical oceanographic practice, that in principle it is unlikely to be helpful, and that it may even be harmful if the data coverage is sparse, the usual case in oceanography. This is in part a consequence of ignorance of the correct error statistics for the data and model, but only in part. The reduced state space is obtained from a truncated set of multivariate empirical orthogonal functions (EOFs) derived from a long model run without assimilation. The reduced state space filter is compared with a full grid point Kalman filter using the same dynamical model for the period 1979-1985, assimilating eight tide gauge stations and using an additional seven for verification (Miller et al., 1995). Results are not inferior to the full grid point filter, even when the reduced filter retains only nine EOFs. Five sets of reduced space filter assimilations are run with all tide gauge data for the period 1975-1992. In each set a different number of EOFs is retained: 5, 9, 17, 32, and 93, accounting for 60, 70, 80, 90, and 99% of the model variance, respectively. Each set consists of 34 runs, in each of which one station is withheld for verification. Comparing each set to the nonassimilation run, the average rms error at the withheld stations decreases by more than 1 cm. The improvement is generally larger for the stations at lowest latitudes. Increasing the number of EOFs increases agreement with data at locations where data are assimilated; the added structures allow better fits locally. In contrast, results at withheld stations are almost insensitive to the number of EOFs retained. We also compare the Kalman filter theoretical error estimates with the actual errors of the assimilations. Features agree on average, but not in detail, a reminder of the fact that the quality of theoretical estimates is limited by the quality of error models they assume. We briefly discuss the implications of our work for future studies, including the application of the method to full ocean general circulation models and coupled models.

Design of an extended Kalman filter frequency tracker

Abstract: The design of an extended Kalman filter for tracking a time-varying frequency is discussed. Its principal modes of failure are explained. The design tradeoff between balancing noise rejection and tracking at a maximal slew rate is discussed. The performance penalties for overdesign and underdesign of noise covariances are examined, and theoretically supported design guidelines are suggested.

Linear estimation in Krein spaces. I. Theory

Abstract: The authors develop a self-contained theory for linear estimation in Krein spaces. The derivation is based on simple concepts such as projections and matrix factorizations and leads to an interesting connection between Krein space projection and the recursive computation of the stationary points of certain second-order (or quadratic) forms. The authors use the innovations process to obtain a general recursive linear estimation algorithm. When specialized to a state-space structure, the algorithm yields a Krein space generalization of the celebrated Kalman filter with applications in several areas such as H/sup /spl infin//-filtering and control, game problems, risk sensitive control, and adaptive filtering.

Markov chain Monte Carlo in conditionally Gaussian state space models

Abstract: SUMMARY A Bayesian analysis is given for a state space model with errors that are finite mixtures of normals and with coefficients that can assume a finite number of different values. A sequence of indicator variables determines which components the errors belong to and the values of the coefficients. The computation is carried out using Markov chain Monte Carlo, with the indicator variables generated without conditioning on the states. Previous approaches use the Gibbs sampler to generate the indicator variables conditional on the states. In many problems, however, there is a strong dependence between the indicator variables and the states causing the Gibbs sampler to converge unacceptably slowly, or even not to converge at all. The new sampler is implemented in O(n)operations, where n is the sample size, permitting an exact Bayesian analysis of problems that previously had no computationally tractable solution. We show empirically that the new sampler can be much more efficient than previous approaches, and illustrate its applicability to robust nonparametric regression with discontinuities and to a time series change point problem.

State bounding with ellipsoidal set description of the uncertainty

Abstract: The paper presents recursive algorithms for state bounding, using ellipsoidal sets to describe the state uncertainties and to bound the process and observation noises The algorithms optimize each stage of updating, according to the minimum-volume and minimum-trace criteria Simulations compare the performance of the bounding algorithms with that of a Kalman filter, and investigate the influence of noise distribution within the bounds

Identification and tracking of harmonic sources in a power system using a Kalman filter

Abstract: In this paper, two problems have been addressed on harmonic sources identification: the optimal locations of a limited number of harmonic meters and the optimal dynamic estimates of harmonic source locations and their injections in unbalanced three-phase power systems. Kalman filtering is used to solve these problems. System error covariance analysis by the Kalman filter associated with a harmonic injection estimate determines the optimal arrangement of limited harmonic meters. Based on the optimally-arranged harmonic metering locations, the Kalman filter then yields the optimal dynamic estimates of harmonic injections with a few noisy harmonic measurements. The method is dynamic and has the capability of identifying, analyzing and tracking each harmonic injection at all buses in unbalanced three-phase power systems. Actual recorded harmonic measurements and simulated data in a power distribution system are provided to prove the efficiency of this approach.

Polynomial Filtering for Linear Discrete Time Non-Gaussian Systems

Abstract: In this work we propose a new filtering approach for linear discrete time non-Gaussian systems that generalizes a previous result concerning quadratic filtering [A. De Santis, A. Germani, and M. Raimondi, IEEE Trans. Automat. Control, 40 (1995) pp. 1274--1278]. A recursive $ u$th-order polynomial estimate of finite memory $\Delta$ is achieved by defining a suitable extended state which allows one to solve the filtering problem via the classical Kalman linear scheme. The resulting estimate will be the mean square optimal one among those estimators that take into account $ u$-polynomials of the last $\Delta$ observations. Numerical simulations show the effectiveness of the proposed method.

The Kalman filter in finance

Abstract: Preface. 1. Introduction. 2. Test for parameter stability. 3. Flexible Least Squares. 4. The Kalman filter. 5. Parameter estimation. 6. The estimates, reconsidered. 7. Modeling with the Kalman filter. A. Tables of references. B. The programs and the data. Bibliography. Index.

A new motor speed estimator using Kalman filter in low-speed range

Abstract: In this paper, a new machine drive technique using novel estimation strategy for the very low-speed operation to estimate both the instantaneous speed and disturbance load torque is proposed. In the proposed algorithm, a Kalman filter is incorporated to estimate both the motor speed and the disturbance torque. The Kalman filter is an optimal state estimator and is usually applied to a dynamic system that involves a random noise environment. The effects of parameter variations are discussed, and it is verified that the system is stable to the modeling error. Experimental results confirm the validity of the proposed estimation technique.

Real time recursive parameter estimation in energy management systems

Abstract: A new method of recursive parameter estimation using a Kalman filter is presented. The method is capable of estimating impedance parameters of network branches in both online and offline modes. It provides accurate estimation of branch parameters in the presence of noise in measurements and has the ability to identify and reject gross measurement errors. The method can track impedance parameters as they fluctuate due to changes in load and ambient conditions. Test results on a 100-bus network as well as results of method's implementation in a real life EMS are reported in the paper.

A new technique for nonlinear estimation

Abstract: A new nonlinear filter referred to as the state-dependent Riccati equation filter (SDREF) is presented. The SDREF is derived by constructing the dual of a little known nonlinear regulator control design technique which involves the solution of a state-dependent Riccati equation (SDRE) and which has been appropriately called the SDRE control method. The resulting SDREF has the same structure as the continuous steady-state linear Kalman filter. In contrast to the linearized Kalman filter (LKF) and the extended Kalman filter (EKF) which are based on linearization, the SDREF is based on a parameterization that brings the nonlinear system to a linear structure having state-dependent coefficients (SDC). In a deterministic setting, before stochastic uncertainties are introduced, the SDC parameterization fully captures the nonlinearities of the system, It was shown in Cloutier et al. (1996) that, in the multivariable case, the SDC parameterization is not unique and that the SDC parameterization itself can be parameterized. This latter parameterization creates extra degrees of freedom that are not available in traditional filtering methods. These additional degrees of freedom can be used to either enhance filter performance, avoid singularities, or avoid loss of observability. The main intent of this paper is to introduce the new nonlinear filter and to illustrate the behaviorial differences and similarities between the new filter, the LKF, and the EKF using a simple pendulum problem.

Approximate Data Assimilation Schemes for Stable and Unstable Dynamics

Abstract: Two suboptimal data assimilation schemes for stable and unstable dynamics are introduced. The first scheme, the partial singular value decomposition filter, is based on the most dominant singular modes of the tangent linear propagator. The second scheme, the partial eigendecomposition filter, is based on the most dominant eigenmodes of the propagated analysis error covariance matrix. Both schemes rely on iterative procedures like the Lanczos algorithm to compute the relevant modes. The performance of these schemes is evaluated for a shallow-water model linearized about an unstable Bickley jet. The results are contrasted against those of a reduced resolution filter, in which the gains used to update the state vector are calculated from a lower-dimensional dynamics than the dynamics that evolve the state itself. The results are also contrasted against the exact results given by the Kalman filter. These schemes are validated for the case of stable dynamics as well. The two new approximate assimilation schemes are shown to perform well with relatively few modes computed. Adaptive tuning of a modeled trailing error covariance for all three of these low-rank approximate schemes enhances performance and compensates for the approximation employed.

An input estimation approach to on-line two-dimensional inverse heat conduction problems

Abstract: An on-line methodology to solve two-dimensional inverse heat conduction problems (IHCP) is presented. A new input estimation approach based on the Kalman filtering technique is developed to estimate the two separate unknown heat flux inputs on the two boundaries in real time. A recursive relation between the observed value of the residual sequence with unknown heat flux and the theoretical residual sequence of the Kalman filter that assumes known heat flux is formulated. A real-time least-squares algorithm is derived that uses the residual innovation sequence to compute the magnitude of heat flux. This recursive approach facilitates practical implementation, and its capabilities are demonstrated in several typical cases with discontinuous and time-varying heat flux inputs.

Optimal guaranteed cost filtering for uncertain discrete-time linear systems

Abstract: This paper presents a result on the design of a steady-state robust state estimator for a class of uncertain discrete-time linear systems with normal bounded uncertainty. This result extends the steady state Kalman filter to the case in which the underlying system is uncertain. A procedure is given for the construction of a state estimator which minimizes a bound on the state error covariance. It is shown that this leads to a state estimator which is optimal with respect to a notion of quadratic guaranteed cost state estimation.

Estimation and equalization of fading channels with random coefficients

Abstract: The time-varying tap coefficients of frequency selective fading channels are typically modeled as random processes with low-pass power spectra. However, traditional adaptive techniques usually make no assumption on the channel's time variations and hence do not exploit this information. Kalman filtering methods are derived to track the channel by employing a multichannel autoregressive description of the time-varying taps in a decision-feedback equalization framework. Fitting a model to the variations of the channel's taps is a challenging task because the tap coefficients are not observed directly. A novel linear method is proposed in order to estimate the model parameters from input/output data. The consistency of the proposed method is shown, and some illustrative simulations are presented.

Monitoring the thermal condition of permanent-magnet synchronous motors

Abstract: This paper describes a novel approach to monitoring the condition of small permanent-magnet synchronous motors (PMSM) operating under thermal stress. The approach begins with the estimation of temperature-dependent motor parameters from measurements of line voltages and currents. The parameters are then used to derive estimates of motor temperatures. Next, the electrically estimated temperatures are combined with a surface measurement of motor temperature and a dynamic thermal model of the motor to yield an observer that is a Kalman filter. The temperatures estimated by the observer are used for failure prevention. Finally, by modifying the observer, it is tuned to use the geometric properties of its innovation for failure detection. The innovation, that is, the difference between the thermally and electrically estimated temperatures, is monitored and compared against appropriate thresholds to detect failures. Failure detection is demonstrated experimentally, and shown to be capable of distinguishing the conditions of normal operation, and operation with obstructed cooling.

Image analyzing device using adaptive criteria

Abstract: A hybrid filter architecture and an artificial neural network is proposed for image enhancement and detection of suspicious areas in digital x-ray images that is operator, image, and digital x-ray sensor independent. The hybrid filter architecture includes an Adaptive Multistage Nonlinear Filter (AMNF) cascaded with an M-channel Tree Structured Wavelet Transform (TSWT). The AMNF shares the advantages of an array of linear and nonlinear filters and is adaptively supervised using either an order statistic or linear operator. The filter is used for noise suppression and image enhancement and adapts to the noise characteristics of the sensor. The TSWT is used for multiresolution image decomposition and reconstruction of subimages for further image enhancement of diagnostic features of interest. A Multistage Artificial Neural Network (MANN) is proposed, together with Kalman Filtering for network training, for both improved detection or classification of suspicious areas and computational efficiency to allow the MANN to be applied to full digital images without operator input. The hybrid filter architecture and MANN may be applied to any gray scale image in medical imaging. The specific application of the proposed method includes: (a) improved enhancement or detection of suspicious areas as a "second opinion" strategy using a computer workstation or (b) mass screening of large image databases such as that used for medical screening programs.