state estimation with partially observed inputs a unified kalman filtering approach for linear stochastic time varying state space models with gaussian noises this paper investigates state estimation for the scenario where the input variables of the state equation are not fully observed but rather the input data are available only at an aggregate level unlike the existing filters for unknown inputs that are based on the approach of minimum variance unbiased estimation this paper does not impose the unbiasedness condition for state estimation instead it incorporates a bayesian approach to derive a modified kalman filter by pooling the prior knowledge about the state vector at the aggregate level with the measurements on the output variables at the original level of interest the estimated state vector is shown to be a minimum mean square error estimator the developed filter provides a unified approach to state estimation it includes the existing filters obtained under two extreme scenarios as its special cases i e the classical kalman filter where all the inputs are observed and the filter for unknown inputs

h_inf functional filtering for linear systems with unknown inputs a new method for the h1 functional filtering for systems with unknown inputs is presented first a functional filtering technique is presented then we show that the dynamic of the estimation error can be modeled in the descriptor system form this formulation makes it possible to describe the dynamic estimation error free of the derivative of the disturbances the filter parameters are obtained from the bounded real lemma of descriptor systems the developed approach unifies the design of functional reduced order and full order filters necessary and sufficient conditions for the solvability of the problem are obtained in terms of a set of bilinear matrix inequalities bmi under certain conditions these inequalities can be transformed to a set of linear matrix inequalities lmi two numerical examples are presented to illustrate the approach described in the paper the first example concerns the estimation of the state of charge soc of the the lithium ion battery and the second example highlights the reduced nature of the proposed functional filter

time domain electromagnetic wave propagation analysis by edge based smoothed point interpolation methods in this work smoothed meshfree methods are firstly applied to numerically analyze two dimensional electromagnetic wave propagation models a weakened weak formulation based on the edges of triangular cells obtained by a delaunay triangulation is considered here framing the so called edge based smoothed domains the meshfree shape functions are computed by the radial point interpolation method rpim considering two schemes for the shape function support domains namely the t3 and the t6 scheme these schemes are based on the above mentioned triangular cells and they consider fixed numbers of nodes for the support domains a new procedure to compute the transient related matrices is discussed here aiming to be more consistent with the edge based smoothed point interpolation method in this new procedure finer local integration techniques are managed to be considered at lower computational costs rendering more accurate and efficient techniques at the end of the paper numerical examples are presented illustrating the effectiveness and potentialities of the proposed methodologies

parity equations based unknown input reconstruction for mimo stochastic systems with an application in this paper an approach for reconstructing an unknown input for multiple input multiple output systems is presented it is assumed that the system is affected by process noise whereas the available system inputs are contaminated by measurement noise the novel approach is based on a parity equations concept and forms an extension of the idea developed previously by the authors a modification of the algorithm is also provided which allows the approach to deal with systems whose zero is close to unity the order of the parity space can be treated as a tuning parameter allowing for a trade off between the smoothness of the reconstructed unknown input and a phase lag an analytical solution of the overall problem is obtained by making use of a lagrange multiplier method the utility of the scheme is demonstrated when applied to a practical hydrological system

a g space theory and a weakened weak w2 form for a unified formulation of compatible and incompatible methods part i theory this paper introduces a g space theory and a weakened weak form w2 using the generalized gradient smoothing technique for a unified formulation of a wide class of compatible and incompatible methods the w2 formulation works for both finite element method settings and mesh free settings and w2 models can have special properties including softened behavior upper bounds and ultra accuracy part i of this paper focuses on the theory and fundamentals for w2 formulations a normed g space is first defined to include both continuous and discontinuous functions allowing the use of much more types of methods techniques to create shape functions for numerical models important properties and a set of useful inequalities for g spaces are then proven in the theory and analyzed in detail these properties ensure that a numerical method developed based on the w2 formulation will be spatially stable and convergent to the exact solutions as long as the physical problem is well posed the theory is applicable to any problems to which the standard weak formulation is applicable and can offer numerical solutions with special properties including close to exact stiffness upper bounds and ultra accuracy copyright 2009 john wiley sons ltd

a stable nodal integration method for static and quasi static electromagnetic field computation a stable nodal integration method snim is presented to solve static and quasi static electromagnetic problems in this paper the analysis domain is firstly discretized into a set of triangular or tetrahedral elements and linear interpolation is adopted within each element a weakened weak formulation based on the nodes is further considered framing the so called node based smoothing domains equivalent smoothing domains are then acquired as circular or spherical regions where the gradient of shape function is expanded as the first order taylor form subsequently four or six temporary integration points on the region are picked to obtain items of the stiffness matrix and the external load vector by simplifying the equations the stiffness matrix can be received in quite concise form with one point integration and stabilization terms which are calculated on original node based smoothing domains the implementation of snim on electromagnetic problems is thus realized the proposed formulation is validated against both analytical solutions and traditional methods and its effectiveness and potentialities can be well represented and clarified by numerical examples

linear filtering for bilinear stochastic differential systems with unknown inputs investigates the problem of state estimation for bilinear stochastic multivariable differential systems in the presence of an additional disturbance whose statistics are completely unknown a linear filter is proposed based on a suitable decomposition of the state of the bilinear system into two components the first one is a computable function of the observations while the second component is estimated via a suitable linear filtering algorithm no a priori information on the disturbance is required for the filter implementation the proposed filter is robust with respect to the unknown input in that the covariance of the estimation error is not affected by such input numerical simulations show the effectiveness of the proposed filter

brief extension of minimum variance estimation for systems with unknown inputs in this paper we address the problem of minimum variance estimation for discrete time time varying stochastic systems with unknown inputs the objective is to construct an optimal filter in the general case where the unknown inputs affect both the stochastic model and the outputs it extends the results of darouach and zasadzinski automatica 33 1997 717 where the unknown inputs are only present in the model the main difficulty in treating this problem lies in the fact that the estimation error is correlated with the systems noises this fact leads generally to suboptimal filters necessary and sufficient conditions for the unbiasedness of this filter are established then conditions under which the estimation error and the system noises are uncorrelated are presented and an optimal estimator and a predictor filters are derived sufficient conditions for the existence of these filters are given and sufficient conditions for their stability are obtained for the time invariant case a numerical example is given in order to illustrate the proposed method

event based state estimation of linear dynamic systems with unknown exogenous inputs in this work an event based optimal state estimation problem for linear time varying systems with unknown inputs is investigated by treating the unknown input as a process with a non informative prior the event based minimum mean square error mmse estimator is obtained in a recursive form it is shown that for the general time varying case the closed loop matrix of the optimal event based estimator is exponentially stable and the estimation error covariance matrix is asymptotically bounded for each sample path of the event triggering process the results are also extended to the multiple sensor scenario where each sensor is allowed to have its own event triggering condition the efficiency of the proposed results is illustrated by a numerical example and comparative simulation with the mmse estimators obtained based on time triggered measurements the results are potentially applicable to event based secure state estimation of cyber physical systems

riccati equations in optimal filtering of nonstabilizable systems having singular state transition matrices until recently it was believed that a necessary and sufficient condition for convergence of the riccati difference equation of optimal filtering was that the system be both delectable and stabilizable recently it has been shown that the stabilizability condition can be removed but convergence has only established under restrictive assumptions including the requirement that the state transition matrix be nonsingular the present paper generalizes these results in several directions first properties of the algebraic riccati equation are established for the case of singular state transition matrix second several assumptions previously imposed in establishing convergence of the riccati difference equation for systems with unreachable modes on the unit circle are relaxed including replacing observability by detectability weakening the conditions on the initial covariance and allowing the state transition matrix to be singular third results on the convergence and properties of the riccati equations are expressed as both necessary and sufficient conditions whereas previous results were only sufficient these extensions mean that the results have wider applicability including fixed lag smoothing problems and filtering for systems with time delays the implications of the results in the dual problem of optimal control are also studied

on existence optimality and asymptotic stability of the kalman filter with partially observed inputs for linear stochastic time varying systems we investigate the properties of the kalman filter with partially observed inputs we first establish the existence condition of a general linear filter when the unknown inputs are partially observed then we examine the optimality of the kalman filter with partially observed inputs finally on the basis of the established existence condition and optimality result we investigate asymptotic stability of the filter for the corresponding time invariant systems it is shown that the results on existence and asymptotic stability obtained in this paper provide a unified approach to accommodating a variety of filtering scenarios as its special cases including the classical kalman filter and state estimation with unknown inputs

surface reconstruction and representation of 3 d scenes abstract this paper describes a method of reconstructing and representing the surface of objects in a 3 d scene from a set of parallel planar slices or cross sections the 3 d scene may consist of several occluding objects each slice may consist of a number of distinct 2 d regions defining the 2 d structure at that particular cross section the method described does not require that the scene description be a completely segmented one i e separate object structures need not be distinguished from each other the method can be used where only incomplete description due to missing data are available the generation of surface description consists of three steps first the contours of 2 d regions are traced and an attempt to segment the 3 d scene is made by associating contours on different slices second a triangulation process is performed over pairs of associated contours to generate the local bounding surface structure finally a surface hierarchy is established by coalescing adjacent triangular patches of same orientation into polygonal facets these facets are then integrated to form the object and scene description the surface structure thus obtained is a collection of polygonal facets which bound the outer surface of the objects

estimation of systems with statistically constrained inputs abstract this paper discusses the estimation of a class of discrete time linear stochastic systems with statistically constrained unknown inputs ui which can represent an arbitrary combination of a class of un modeled dynamics random ui with unknown covariance matrix and deterministic ui in filter design an upper bound filter is explored to compute recursively and adaptively the upper bounds of covariance matrices of the state prediction error innovation and state estimate error furthermore the minimum upper bound filter mubf is obtained via online scalar parameter convex optimization in pursuit of the minimum upper bounds two examples a system with multiple piecewise uis and a continuous stirred tank reactor cstr are used to illustrate the proposed mubf scheme and verify its performance

recognizing cylindrical objects by single camera views a new approach to recognition of cylindrical objects by single camera views using camera calibration surface backprojection and model matching techniques is proposed cylindrical objects to be recognized can be of different radii and heights both the silhouette shapes and the surface patterns of objects are utilized in the recognition scheme a new camera calibration technique is first employed to compute the camera parameters analytically using a single camera view of the object a surface backprojection technique is then adopted to reconstruct the pattern on the surface patch of the input object the reconstructed surface pattern is finally matched with that of each object model using a partial shape matching technique to find the best matching surface patch pattern of the models from which the input object is classified accordingly experimental results showing the feasibility of the proposed approach are also included

brief paper unbiased minimum variance state estimation for linear systems with unknown input the problem of state estimation for a linear system with unknown input which affects both the system and the output is discussed in this paper a recursive optimal filter with global optimality in the sense of unbiased minimum variance over all linear unbiased estimators is provided the necessary and sufficient condition for the convergence and stability is also given which is milder than existing approaches

simultaneous input and state estimation for nonlinear systems with applications to flow field estimation this paper studies the problem of simultaneous input and state estimation sise for nonlinear dynamical systems with and without direct input output feedthrough we take a bayesian perspective to develop a sequential joint input and state estimation approach our scheme gives rise to a nonlinear maximum a posteriori optimization problem which we solve using the classical gauss newton method the proposed approach generalizes a number of sise methods presented in the literature we illustrate the effectiveness of the proposed scheme for nonlinear systems with direct feedthrough in an oceanographic flow field estimation problem involving submersible drogues that measure position intermittently and acceleration continuously

technical communique on the asymptotic stability of minimum variance unbiased input and state estimation in this note we investigate the asymptotic stability of the filter for minimum variance unbiased input and state estimation developed by gillijns and de moor sufficient conditions for the stability are proposed and proven with inspiration from the kalman filter stability analysis

extension of friedland s separate bias estimation to randomly time varying bias of nonlinear systems by extending b friedland s 1969 separate bias estimation algorithm for linear systems to nonlinear systems and combining the result with the suboptimal fading extended kalman filter proposed by d h zhou 1990 and by d h zhou et al a pseudo separate bias estimation algorithm for randomly time varying bias of a class of nonlinear time varying stochastic systems is obtained a simulation example is presented to illustrate the effectiveness of the algorithm

technical communique reduced order kalman filter with unknown inputs this paper presents an optimal reduced order kalman filter for discrete time dynamic stochastic linear systems with unknown inputs the problem is to estimate a part of the state vector in the case where none of the observations are assumed to be noise free the proposed filter is obtained by minimizing the trace of the estimation error covariance matrix with respect to the remaining degrees of freedom after noninteresting state and unknown inputs decoupling the necessary and sufficient conditions for stability and convergence of the filter are established

curvature based fuzzy surface classification in this paper a fuzzy surface classification paradigm which is an extension to the conventional techniques based on the sign of the mean h and gaussian k curvatures respectively is presented with the conventional methods two of the major problems that limit object descriptions are 1 their inability to describe surfaces in a natural way and 2 computation of curvatures being highly sensitive to noise as well as limited by resolution problem 1 is addressed by treating the transitional regions between distinct surface types as smoothly varying fuzzy surface types problem 2 gets partially resolved while fuzzifying the signs of the surface curvatures for surface description the new segmentation technique is demonstrated in a model based object recognition system and its performance is compared with a system based on conventional surface classification

an edge based smoothed finite element method es fem for static free and forced vibration analyses of solids abstract this paper presents an edge based smoothed finite element method es fem to significantly improve the accuracy of the finite element method fem without much changing to the standard fem settings the es fem can use different shape of elements but prefers triangular elements that can be much easily generated automatically for complicated domains in the es fem the system stiffness matrix is computed using strains smoothed over the smoothing domains associated with the edges of the triangles intensive numerical results demonstrated that the es fem possesses the following excellent properties 1 the es fem model possesses a close to exact stiffness it is much softer than the overly stiff fem and much stiffer than the overly soft ns fem model 2 the results are often found superconvergence and ultra accurate much more accurate than the linear triangular elements of fem and even more accurate than those of the fem using quadrilateral elements with the same sets of nodes 3 there are no spurious non zeros energy modes found and hence the method is also temporally stable and works well for vibration analysis and 4 the implementation of the method is straightforward and no penalty parameter is used and the computational efficiency is better than the fem using the same sets of nodes in addition a novel domain based selective scheme is proposed leading to a combined es ns fem model that is immune from volumetric locking and hence works very well for nearly incompressible materials these properties of the es fem are confirmed using examples of static free and forced vibration analyses of solids

brief paper unbiased minimum variance input and state estimation for linear discrete time systems this paper addresses the problem of simultaneously estimating the state and the input of a linear discrete time system a recursive filter optimal in the minimum variance unbiased sense is developed where the estimation of the state and the input are interconnected the input estimate is obtained from the innovation by least squares estimation and the state estimation problem is transformed into a standard kalman filtering problem necessary and sufficient conditions for the existence of the filter are given and relations to earlier results are discussed

framework for state and unknown input estimation of linear time varying systems the design of unknown input decoupled observers and filters requires the assumption of an existence condition in the literature this paper addresses an unknown input filtering problem where the existence condition is not satisfied instead of designing a traditional unknown input decoupled filter a double model adaptive estimation approach is extended to solve the unknown input filtering problem it is proved that the state and the unknown inputs can be estimated and decoupled using the extended double model adaptive estimation approach without satisfying the existence condition numerical examples are presented in which the performance of the proposed approach is compared to methods from literature

polyhedron recognition using three view analysis abstract using three view analysis this paper proposes a new approach for single polyhedron recognition both the 3d surface data and 2d image projection patterns are used to find the exact corresponding model of an unknown input object the recognition process first applies a hypothesis generation verification process to match the 3d partial object of the unknown input which is reconstructed by applying the three view matching strategy chiou et al proc ics 88 pp 1516 1521 1988 and the polyhedron reconstruction process hung et al pattern recognition 22 231 246 1989 against the data based models for selecting the possible corresponding models candidates it then backprojects each candidate onto the three image planes and matches the outer boundaries of the three backprojections by a simple method for judging the exact corresponding model measurement error is also analyzed theoretically to give a bound of the threshold value so that it can be automatically chosen in each decision making process the approach is implemented on a pc level vision system experimental results show that the system is feasible and reliable even in a noisy environment

deadbeat unknown input state estimation and input reconstruction for linear discrete time systems abstract this paper considers discrete time input reconstruction and state estimation assuming that the system has no invariant zeros without assuming that the initial condition is known and without assuming that at least one markov parameter has full column rank algorithms based on the generalized inverse of a block toeplitz matrix are given for unknown input state estimation and simultaneous input reconstruction and state estimation in both cases the unknown input is an arbitrary signal both algorithms are deadbeat which means that exact input reconstruction and state estimation are achieved in a finite number of steps

simultaneous input and state estimation for stochastic nonlinear systems with additive unknown inputs abstract this paper investigates simultaneous input and state estimation for a class of nonlinear stochastic systems we propose a recursive filter to concurrently estimate system states and unknown inputs we show that the estimation errors of the proposed filter are practically exponentially stable in probability and the estimation error covariance matrices are uniformly bounded

the optimal projection equations for reduced order state estimation first order necessary conditions for optimal steady state reduced order state estimation for a linear time invariant plant in the presence of correlated disturbance and nonsingular measurement noise are derived in a new and highly simplified form in contrast to the lone matrix riccati equation arising in the full order kalman filter case the optimal steady state reduced order estimator is characterized by three matrix equations one modified riccati equation and two modified lyapunov equations coupled by a projection whose rank is precisely equal to the order of the estimator and which determines the optimal estimator gains this coupling is a graphic reminder of the suboptimality of proposed approaches involving either model reduction followed by full order estimator design or full order estimator design followed by estimator reduction techniques the results given here complement recently obtained results which characterize the optimal reduced order model by means of a pair of coupled modified lyapunov equations 7 and the optimal fixed order dynamic compensator by means of a coupled system of two modified riceati equations and two modified lyapunov equations 6

a robust estimator for stochastic systems under unknown persistent excitation a robust estimator for uncertain stochastic systems under unknown persistent disturbance is presented the given discrete time stochastic formulation neither requires a known bound on the magnitude of the unknown excitation nor assumes stability of the system however the proposed estimator assumes certain structural conditions on system uncertainties though the proposed estimator is developed based on stochastic lyapunov analysis its structure and performance are comparable to that of unbiased minimum variance filters based on the disturbance decoupling technique unlike unbiased minimum variance filters implementation of the developed estimator only requires adding an auxiliary term to the nominal steady state kalman filter and it does not involve any similarity transformation or propagation of matrix difference equations

state estimation for stochastic discrete time systems with multiplicative noises and unknown inputs over fading channels abstract this paper deals with robust state estimation problem for a class of stochastic discrete time systems with multiplicative noises and unknown inputs over fading channels an unbiased unknown input insensitive filter is designed such that the variance of the estimation error is minimized in the sense of the so called p estimation the filter gain matrix is derived through solving a recursive riccati equation and a generalized lyapunov equation a necessary and sufficient condition that guarantees the existence of the filter is given which establishes a fundamental limit on the mean square capacity of each fading channel unknown input estimation and finite horizon stability of the proposed filter are also discussed to illustrate the effectiveness of the proposed approach the proposed algorithm is applied to a faulty remote controlled uninterruptible power system where both the state and the fault are estimated

a unified filter for simultaneous input and state estimation of linear discrete time stochastic systems in this paper we present a unified optimal and exponentially stable filter for linear discrete time stochastic systems that simultaneously estimates the states and unknown inputs in an unbiased minimum variance sense without making any assumptions on the direct feedthrough matrix we also provide the connection between the stability of the estimator and a system property known as strong detectability and discuss the global optimality of the proposed filter finally an illustrative example is given to demonstrate the performance of the unified unbiased minimum variance filter

reduced order kalman filter for a continuous time fractional order system using fractional order average derivative abstract this paper investigates two kinds of reduced order kalman filters for a continuous time fractional order system with uncorrelated and correlated process and measurement noises the fractional order average derivative is adopted to enhance the discretization accuracy for the investigated continuous time fractional order system the uncorrelated and correlated cases for the process and measurement noises are treated by the reduced order kalman filters to achieve the robust estimation for a part of states of a fractional order system the truncation issue is considered to implement the practical application of the proposed state estimation algorithm finally two examples for uncorrelated and correlated noises are offered to verify the effectiveness of the proposed reduced order kalman filters

optimal filtering for systems with unknown inputs via the descriptor kalman filtering method this paper addresses globally optimal unbiased minimum variance state estimation for systems with unknown inputs that affect both the system and the output with the descriptor kalman filtering method it is shown that directly applying the conventional descriptor kalman filter dkf to the considered problem may not yield the globally optimal solution because the unknown input vector may not be estimable to remedy this problem three approaches are proposed to facilitate optimal filter design the transformed approach uses some input and output transformations the untrammeled approach does not require any transformations and the augmented approach reconstructs the unknown input dynamics then three 5 block forms of the extended dkf 5 block edkf are derived as globally optimal state estimators in the sense that the first two filters are equivalent to the recently developed extended recursive three step filter and the third is equivalent to the conventional augmented state kalman filter the relationship between the proposed edkfs and the existing results in the literature is addressed simulation results are given to illustrate the usefulness of the proposed filters

technical communique extension of unbiased minimum variance input and state estimation for systems with unknown inputs this paper extends the existing results on joint input and state estimation to systems with arbitrary unknown inputs the objective is to derive an optimal filter in the general case where not only unknown inputs affect both the system state and the output but also the direct feedthrough matrix has arbitrary rank the paper extends both the results of gillijns and de moor gillijns s de moor b 2007b unbiased minimum variance input and state estimation for linear discrete time systems with direct feedthrough automatica 43 934 937 and darouach zasadzinski and boutayeb darouach m zasadzinski m boutayeb m 2003 extension of minimum variance estimation for systems with unknown inputs automatica 39 867 876 the resulting filter is an extension of the recursive three step filter ertsf and serves as a unified solution to the addressed unknown input filtering problem the relationship between the ertsf and the existing literature results is also addressed

an edge based smoothed finite element method es fem for analyzing three dimensional acoustic problems abstract it is well known that one key issue of solving the helmholtz equation using finite element method fem is the accuracy deterioration in the solution with increasing wave number due to the numerical dispersion error such a numerical dispersion error is essentially caused by the overly stiff nature of the fem model to overcome this problem this paper presents an edge based smoothed finite element method es fem for analyzing acoustic problems using linear triangular and tetrahedron elements that can be generated automatically respectively for complicated two dimensional and three dimensional domains the discretized linear system equations for es fem are established using the smoothed galerkin weak form with smoothing domains associated with the edges of the triangles or surfaces of the tetrahedrons the edge based gradient smoothing operation provides proper softening effect makes the es fem model much softer than the overly stiff fem model and hence significantly reduces the numerical dispersion error numerical examples including a 2d problem of acoustic pressure distribution in a vehicle passenger compartment and a 3d problem about the acoustic pressure distribution in an engine chamber have been studied using the present es fem the results demonstrate that the es fem possesses the following advantages compared with the standard fem using the same meshes first es fem achieves similar convergence rate but better accuracy especially at high frequency second es fem is less sensitive to the mesh distortion meaning that the quality of mesh has less effect on the solution of es fem third it works well for triangular types of meshes and thus for the problems with complicated geometry

unbiased minimum variance linear state estimation abstract a method is developed for linear estimation in the presence of unknown or highly non gaussian system inputs the state update is determined so that it is unaffected by the unknown inputs the filter may not be globally optimum in the mean square error sense however it performs well when the unknown inputs take extreme or unexpected values in many geophysical and environmental applications it is performance during these periods which counts the most the application of the filter is illustrated in the real time estimation of mean areal precipitation

experiments in computing optical flow with the gradient based multiconstraint method abstract in this paper we present results on experiments in computing optical flow with the multiconstraint approach this approach relies on the gradient equation which relates spatial and temporal changes in the image to optical flow and the use of various image functions the experiments reported here are performed on camera acquired pictures and use feature operators to derive image functions from the original luminance function results indicate that this approach is promising

observers for linear systems with unknown inputs a direct design procedure of a full order observer for a linear system with unknown inputs is presented using straightforward matrix calculations some examples are given in these examples a reduced order observer is also derived it is shown that this may restrict the rate of convergence of some state estimates

delayed unknown input observers for discrete time linear systems with guaranteed performance abstract in this paper we propose a state and unknown input observer for discrete time linear systems with bounded unknown inputs and measurement disturbances the design procedure is formulated using a set of linear matrix inequalities and leverages delayed or fixed lag estimates the observer error states and or user defined performance outputs are guaranteed to operate at certain performance bounds furthermore by employing sufficiently large delays the observer is guaranteed to provide exact asymptotic state and input estimates for minimum phase systems we demonstrate via numerical examples that the proposed observer can be used for a wider class of systems than those satisfying matching conditions

a linearly conforming point interpolation method lc pim for three dimensional elasticity problems linearly conforming point interpolation method lc pim is formulated for three dimensional elasticity problems in this method shape functions are generated using point interpolation method by adopting polynomial basis functions and local supporting nodes are selected based on the background cells the shape functions so constructed have the kronecker delta functions property and it allows straightforward imposition of point essential boundary conditions galerkin weak form is used for creating discretized system equations and a nodal integration scheme with strain smoothing operation is used to perform the numerical integration the present lc pim can guarantee linear exactness and monotonic convergence for the numerical results numerical examples are used to examine the present method in terms of accuracy convergence and efficiency compared with the finite element method using linear elements the lc pim can achieve better efficiency and higher accuracy especially for stresses copyright 2007 john wiley sons ltd

kalman filter for discrete time stochastic linear systems subject to intermittent unknown inputs state estimation of stochastic discrete time linear systems subject to persistent unknown inputs has been widely studied but only few works have been dedicated to the case where unknown inputs may be simultaneously or sequentially active or inactive in this technical note a kalman filter approach is proposed for state estimation of systems with unknown intermittent inputs the design is based on the minimisation of the trace of the state estimation error covariance matrix under the constraint that the state estimation error is decoupled from the unknown inputs corrupting the system at the current time the necessary and sufficient stability conditions are established considering the upper bound of the prediction error covariance matrix

identification of space curves from two dimensional perspective views this paper describes a new method to be used for matching three dimensional objects with curved surfaces to two dimensional perspective views the method requires for each three dimensional object a stored model consisting of a closed space curve representing some characteristic connected curved edges of the object the input is a two dimensional perspective projection of one of the stored models represented by an ordered sequence of points the input is converted to a spline representation which is sampled at equal intervals to derive a curvature function the fourier transform of the curvature function is used to represent the shape the actual matching is reduced to a minimization problem which is handled by the levenberg marquardt algorithm 3

full order observers for linear systems with unknown inputs this note presents a simple method to design a full order observer for linear systems with unknown inputs the necessary and sufficient conditions for the existence of the observer are given

on the equivalence between the unbiased minimum variance estimation and the infinity augmented kalman filter in this paper the augment kalman filter akf approach is discussed under the infinity condition it is proved that the akf in infinity condition is equivalent to the minimum variance unbiased mv

unbiased minimum variance estimation for systems with unknown exogenous inputs a new method is developed for the state estimation of linear discrete time stochastic systems in the presence of an unknown disturbance the filter obtained is optimal in the unbiased minimum variance sense the necessary and sufficient conditions for the existence and the stability of the filter are given

matching three dimensional objects using silhouettes a method for matching three dimensional objects against a library of models from an observed sequence of silhouettes is presented in this correspondence based upon the observed silhouettes the three dimensional structure of the object is constructed and refined the principal moments and three primary silhouettes are computed for the constructed three dimensional objects to represent the aggregate and detailed structure parameters the adaptive matching technique requires that sufficient silhouettes be added to modify the structure of the unknown object until consistent and steady matching results are obtained the library for matching is based on three primary silhouettes of the model objects experiments conducted show a fast convergence to a consistent result may be achieved provided that a reasonable choice of silhouettes is made

technical communique two stage kalman estimator with unknown exogenous inputs this paper presents a two stage estimator for bias and state filtering in discrete time stochastic linear systems affected by unknown inputs or disturbances we show that the state estimate can be expressed as x k k x k k b k kb k k where x k k is a bias free state estimate and b k k the optimal estimate of constant bias the proposed two stage estimator is based on an alternate derivation of the unbiased minimum variance estimator with unknown exogenous inputs developed by darouach and zasadzinski 1997 automatica 33 717 719

the visual hull concept for silhouette based image understanding many algorithms for both identifying and reconstructing a 3 d object are based on the 2 d silhouettes of the object in general identifying a nonconvex object using a silhouette based approach implies neglecting some features of its surface as identification clues the same features cannot be reconstructed by volume intersection techniques using multiple silhouettes of the object this paper addresses the problem of finding which parts of a nonconvex object are relevant for silhouette based image understanding for this purpose the geometric concept of visual hull of a 3 d object is introduced this is the closest approximation of object s that can be obtained with the volume intersection approach it is the maximal object silhouette equivalent to s i e which can be substituted for s without affecting any silhouette only the parts of the surface of s that also lie on the surface of the visual hull can be reconstructed or identified using silhouette based algorithms the visual hull depends not only on the object but also on the region allowed to the viewpoint two main viewing regions result in the external and internal visual hull in the former case the viewing region is related to the convex hull of s in the latter it is bounded by s the internal visual hull also admits an interpretation not related to silhouettes algorithms for computing visual hulls are presented and their complexity analyzed in general the visual hull of a 3 d planar face object turns out to be bounded by planar and curved patches

brief paper on the global optimality of unbiased minimum variance state estimation for systems with unknown inputs in this paper a globally optimal filtering framework is developed for unbiased minimum variance state estimation for systems with unknown inputs that affect both the system state and the output the resulting optimal filters are globally optimal within the unbiased minimum variance filtering over all linear unbiased estimators globally optimal state estimators with or without output and or input transformations are derived through the global optimality evaluation of this research the performance degradation of the filter proposed by darouach zasadzinski and boutayeb darouach m zasadzinski m boutayeb m 2003 extension of minimum variance estimation for systems with unknown inputs automatica 39 867 876 is clearly illustrated and the global optimality of the filter proposed by gillijns and de moor gillijns s de moor b 2007b unbiased minimum variance input and state estimation for linear discrete time systems with direct feedthrough automatica 43 934 937 is further verified the relationship with the existing literature results is addressed a unified approach to design a specific globally optimal state estimator that is based on the desired form of the distribution matrix from the unknown input to the output is also presented a simulation example is given to illustrate the proposed results

technical communique on the optimality of recursive unbiased state estimation with unknown inputs for system models with unknown inputs state estimates may be obtained using linear minimum variance unbiased estimation here a proof is presented showing that the optimal solution over the class of all linear unbiased estimates may be written in the form of a linear recursive filter thereby validating previous work in this area

unbiased minimum variance fault and state estimation for linear discrete time varying two dimensional systems the fault and state estimation problem is addressed for a class of linear discrete time varying two dimensional systems subject to state and measurement noises two estimators are proposed to compute the estimation of the system state and or fault recursively both of which are unbiased with minimum variance through formulating the estimation problem as the solvability problem of the corresponding matrix equations of estimator gains and system constraint the necessary and sufficient condition of the existence and the solution for the proposed estimators are given an example is used to demonstrate the effectiveness of the proposed estimators