recursive maximum likelihood identification of jump markov nonlinear systems we present an online method for joint state and parameter estimation in jump markov non linear systems jmnls state inference is enabled via the use of particle filters which makes the method applicable to a wide range of non linear models to exploit the inherent structure of jmnls we design a rao blackwellized particle filter rbpf where the discrete mode is marginalized out analytically this results in an efficient implementation of the algorithm and reduces the estimation error variance the proposed rbpf is then used to compute recursively in time smoothed estimates of complete data sufficient statistics together with the online expectation maximization algorithm this enables recursive identification of unknown model parameters including the transition probability matrix the method is also applicable to online identification of jump markov linear systems jmls the performance of the method is illustrated in simulations and on a localization problem in wireless networks using real data

adaptive control of linear systems with markov perturbations the stochastic model considered is a linear jump diffusion process x for which the coefficients and the jump processes depend on a markov chain z with finite state space first we study the optimal filtering and control problem for these systems with non gaussian initial conditions given noisy observations of the state x and perfect measurements of z we derive a new sufficient condition which ensures the existence and the uniqueness of the solution of the nonlinear stochastic differential equations satisfied by the output of the filter we study a quadratic control problem and show that the separation principle holds next we investigate an adaptive control problem for a state process x defined by a linear diffusion for which the coefficients depend on a markov chain the processes x and z being observed in independent white noises suboptimal estimates for the process x z and approximate control law are investigated for a large class of probability distributions of the initial state asymptotic properties of these filters and this control law are obtained upper bounds for the corresponding error are given

state estimation in stochastic hybrid systems with sparse observations in this note we study the problem of state estimation for a class of sampled measurement stochastic hybrid systems where the continuous state x satisfies a linear stochastic differential equation and noisy measurements y are taken at assigned discrete time instants the parameters of both the state and measurement equation depend on the discrete state q of a continuous time finite markov chain even in the fault detection setting we consider at most one transition for q is admissible the switch may occur between two observations whence it turns out that the optimal estimates cannot be expressed in parametric form and time integrations are unavoidable so that the known estimation techniques cannot be applied we derive and implement an algorithm for the estimation of the states x q and of the discrete state switching time that is convenient for both recursive update and the eventual numerical quadrature numerical simulations are illustrated

partial non gaussian state space summary in this paper we suggest the use of simulation techniques to extend the applicability of the usual gaussian state space filtering and smoothing techniques to a class of nongaussian time series models this allows a fully bayesian or maximum likelihood analysis of some interesting models including outlier models discrete markov chain components multiplicative models and stochastic variance models finally we discuss at some length the use of a non gaussian model to seasonally adjust the published money supply figures

extended object tracking using monte carlo methods this correspondence addresses the problem of tracking extended objects such as ships or a convoy of vehicles moving in urban environment two monte carlo techniques for extended object tracking are proposed an interacting multiple model data augmentation imm da algorithm and a modified version of the mixture kalman filter mkf of chen and liu called the mixture kalman filter modified mkfm the data augmentation da technique with finite mixtures estimates the object extent parameters whereas an interacting multiple model imm filter estimates the kinematic states position and speed of the manoeuvring object next the system model is formulated in a partially conditional dynamic linear pcdl form this affords us to propose two latent indicator variables characterizing respectively the motion mode and object size then an mkfm is developed with the pcdl model the imm da and the mkfm performance is compared with a combined imm particle filter imm pf algorithm with respect to accuracy and computational complexity the most accurate parameter estimates are obtained by the da algorithm followed by the mkfm and pf

another neural network based approach for computing eigenvalues and eigenvectors of real skew symmetric matrices this paper introduces a novel neural network based approach for extracting the eigenvalues with the largest or smallest modulus of real skew symmetric matrices as well as the corresponding eigenvectors to this end unlike the previous neural network based methods that can be summarized by some 2n dimensional ordinary differential equations odes where n is the order of the given skew symmetric matrix our proposed approach corresponds to an ode of order n instead of 2n hence the scale of networks can be reduced a lot simulations verify the computational capability of such approach

state estimation for discrete time markov jump linear systems with time correlated measurement noise abstract in this paper the state estimation problem for discrete time markov jump linear systems affected by time correlated measurement noise is considered where the time correlated measurement noise is described by a linear system model with white noise as a result two algorithms are proposed to estimate the state of the system under consideration based on a measurement sequence the first algorithm is optimal in the sense of minimum mean square error which is obtained based on the measurement differencing method bayes rule and some results derived in this paper the second algorithm is a suboptimal algorithm obtained by using a lot of gaussian hypotheses the proposed suboptimal algorithm is finite dimensionally computable and does not increase computational and storage load with time computer simulations are carried out to evaluate the performance of the proposed suboptimal algorithm

identification of time varying systems in reproducing kernel hilbert spaces we consider a class of linear systems in state space form whose parameters evolve in time according to a continuous time gauss markov process our problem is to identify the system from a finite set of noisy output measurements we derive a connection between this problem and tikhonov regularization in reproducing kernel hilbert spaces relying upon this result conditions that ensure the existence of the maximum a posteriori estimate of the parameter trajectory are provided and an identification algorithm is worked out simulated data are used to test the goodness of the proposed approach

quartic splines solutions for a system of second order boundary value problems we use uniform quartic polynomial splines to develop a numerical method for computing smooth approximations to the solution and its derivatives for a system of second order boundary value problems associated with obstacle unilateral and contact problems we show that the present method gives approximations which are better than those produced by other collocation finite difference and spline methods

the use of cubic splines in the numerical solution of a system of second order boundary value problems abstract we use uniform cubic polynomial splines to develop some consistency relations which are then used to develop a numerical method for computing smooth approximations to the solution and its derivatives for a system of second order boundary value problems associated with obstacle unilateral and contact problems we show that the present method gives approximations which are better than those produced by other collocation finite difference and spline methods

a monte carlo approach to nonnormal and nonlinear state space modeling abstract a solution to multivariate state space modeling forecasting and smoothing is discussed we allow for the possibilities of nonnormal errors and nonlinear functionals in the state equation the observational equation or both an adaptive monte carlo integration technique known as the gibbs sampler is proposed as a mechanism for implementing a conceptually and computationally simple solution in such a framework the methodology is a general strategy for obtaining marginal posterior densities of coefficients in the model or of any of the unknown elements of the state space missing data problems including the k step ahead prediction problem also are easily incorporated into this framework we illustrate the broad applicability of our approach with two examples a problem involving nonnormal error distributions in a linear model setting and a one step ahead prediction problem in a situation where both the state and observational equations are nonlinear and involve unknown parameters

stationary filter for linear minimum mean square error estimator of discrete time markovian jump systems we derive in this paper a stationary filter for the linear minimum mean square error estimator lmmse of discrete time markovian jump linear systems mjlss we obtain the convergence of the error covariance matrix of the lmmse to a stationary value under the assumption of mean square stability of the mjls and ergodicity of the associated markov chain it is shown that there exists a unique solution for the stationary riccati filter equation and moreover this solution is the limit of the error covariance matrix of the lmmse the advantage of this scheme is that it is very easy to implement and all calculations can be performed off line leading to a linear time invariant filter

rao blackwellized gaussian smoothing in this paper we consider rao blackwellization of linear substructures in sigma point based gaussian assumed density smoothers we derive marginalized prediction smoothing and update steps for the mixed linear nonlinear gaussian state space model as well as for a hierarchical model for both conventional and iterated posterior linearization gaussian smoothers the proposed method is evaluated in a numerical example and it is shown that the computational complexity is reduced considerably compared to non rao blackwellized gaussian smoothers for systems with high dimensional linear subspaces

a class of fast exact bayesian filters in dynamical models with jumps we address the statistical filtering problem in dynamical models with jumps when a particular application is adequately modeled by linear and gaussian probability density functions with jumps a usual method consists in approximating the optimal bayesian estimate in the sense of the minimum mean square error mmse in a linear and gaussian jump markov state space system jmss practical solutions include algorithms based on numerical approximations or on sequential monte carlo smc methods in this paper we propose a class of alternative methods which consists in building statistical models which locally similarly model the problem of interest but in which the computation of the mmse estimate can be be computed exactly without numerical nor smc approximations and at a computational cost which is linear in the number of observations

the interacting multiple model algorithm for systems with markovian switching coefficients an important problem in filtering for linear systems with markovian switching coefficients dynamic multiple model systems is the management of hypotheses which is necessary to limit the computational requirements a novel approach to hypotheses merging is presented for this problem the novelty lies in the timing of hypotheses merging when applied to the problem of filtering for a linear system with markovian coefficients the method is an elegant way to derive the interacting multiple model imm algorithm evaluation of the imm algorithm shows that it performs well at a relatively low computational load these results imply a significant change in the state of the art of approximate bayesian filtering for systems with markovian coefficients

on adaptive evd asymptotic distribution of centro symmetric covariance matrices this article investigates the gain in statistical performance complexity of the adaptive estimation of the eigenvalue decomposition evd of covariance matrices when the centro symmetric cs structure of such matrices is utilized after deriving the asymptotic distribution of the evd estimators it is shown in particular that the closed form expressions for the asymptotic covariance of batch and adaptive evd estimators are very similar provided that the number of samples is replaced by the inverse of the step size

expectation maximization algorithms for map estimation of jump markov linear systems in a jump markov linear system the state matrix observation matrix and the noise covariance matrices evolve according to the realization of a finite state markov chain given a realization of the observation process the aim is to estimate the state of the markov chain assuming known model parameters computing conditional mean estimates is infeasible as it involves a cost that grows exponentially with the number of observations we present three expectation maximization em algorithms for state estimation to compute maximum a posteriori map state sequence estimates which are also known as bayesian maximum likelihood state sequence estimates mlses the first em algorithm yields the map estimate for the entire sequence of the finite state markov chain the second em algorithm yields the map estimate of the continuous state of the jump linear system the third em algorithm computes the joint map estimate of the finite and continuous states the three em algorithms optimally combine a hidden markov model hmm estimator and a kalman smoother ks in three different ways to compute the desired map state sequence estimates unlike the conditional mean state estimates which require computational cost exponential in the data length the proposed iterative schemes are linear in the data length

modal estimation in hybrid systems in hybrid systems estimation is made difficult by the nonlinear equations of state evolution several algorithms for generating an approximation to the conditional mean estimator have been proposed these are satisfactory in many cases but because of the way the observations are fused the estimate of the modal component of the state is often less good this paper proposes a novel algorithm for state estimation and compares it with some in current use

adaptive gaussian sum filter for nonlinear bayesian estimation a nonlinear filter is developed by representing the state probability density function by a finite sum of gaussian density kernels whose mean and covariance are propagated from one time step to the next using linear system theory methods such as extended kalman filter or unscented kalman filter the novelty in the proposed method is that the weights of the gaussian kernels are updated at every time step by solving a convex optimization problem posed by requiring the gaussian sum approximation to satisfy the fokker planck kolmogorov equation for continuous time dynamical systems and the chapman kolmogorov equation for discrete time dynamical systems the numerical simulation results show that updating the weights of different mixture components during propagation mode of the filter not only provides us with better state estimates but also with a more accurate state probability density function

iterative algorithms for state estimation of jump markov linear systems jump markov linear systems jmlss are linear systems whose parameters evolve with time according to a finite state markov chain given a set of observations our aim is to estimate the states of the finite state markov chain and the continuous in space states of the linear system in this paper we present original deterministic and stochastic iterative algorithms for optimal state estimation of jmlss the first stochastic algorithm yields minimum mean square error mmse estimates of the finite state space markov chain and of the continuous state of the jmls a deterministic and a stochastic algorithm are given to obtain the marginal maximum a posteriori mmap sequence estimate of the finite state markov chain finally a deterministic and a stochastic algorithm are derived to obtain the mmap sequence estimate of the continuous state of the jmls computer simulations are carried out to evaluate the performance of the proposed algorithms the problem of deconvolution of bernoulli gaussian bg processes and the problem of tracking a maneuvering target are addressed

on gibbs sampling for state space models summary we show how to use the gibbs sampler to carry out bayesian inference on a linear state space model with errors that are a mixture of normals and coefficients that can switch over time our approach simultaneously generates the whole of the state vector given the mixture and coefficient indicator variables and simultaneously generates all the indicator variables conditional on the state vectors the states are generated efficiently using the kalman filter we illustrate our approach by several examples and empirically compare its performance to another gibbs sampler where the states are generated one at a time the empirical results suggest that our approach is both practical to implement and dominates the gibbs sampler that generates the states one at a time

hybrid filtering for linear systems with non gaussian disturbances this paper is concerned with hybrid filtering of a non gaussian system coupled by a hidden switching process an optimal control approach is used to derive a finite dimensional recursive filter that is optimal in the sense of the most probable trajectory mpt estimate an approximate scheme is obtained when the probability distribution of the switching process converges to a limiting distribution which is often easier to obtain numerical examples are considered and computational experiments are reported in these examples our schemes outperform popular filtering schemes available in the literature

state and mode estimation for discrete time jump markov systems in this article we compute new state and mode estimation algorithms for discrete time gauss markov models whose parameter sets switch according to a known markov law an important feature of our algorithms is that they are based upon the exact filter dynamics computed in r j elliott f dufour and d sworder ieee trans automat control 41 1996 pp 1807 1810 the fundamental and well known obstacle in estimation of jump markov systems is managing the geometrically growing history of candidate hypotheses in our scheme we address this issue by proposing an extension of an idea due to viterbi our scheme maintains a fixed number of candidate paths in a history each identified by an optimal subset of estimated mode probabilities we compute finite dimensional suboptimal filters and smoothers which estimate the hidden state process and the mode probability our smoothers are based upon a duality between forward and backward dynamics further our smoothing algorithms are general and can be configured into the standard forms of fixed point fixed lag and fixed interval smoothers a computer simulation is included to demonstrate performance

maximum likelihood estimation of transition probabilities of jump markov linear systems this paper describes an online maximum likelihood estimator for the transition probabilities associated with a jump markov linear system jmls the maximum likelihood estimator is derived using the reference probability method which exploits an hypothetical probability measure to find recursions for complex expectations expectation maximization em procedure is utilized for maximizing the likelihood function in order to avoid the exponential increase in the number of statistics of the optimal em algorithm we make interacting multiple model imm type approximations the resulting method needs the mode weights of an imm filter with n3 components where n is the number of models in the jmls the algorithm can also supply base state estimates and covariances as a by product the performance of the estimator is illustrated on two simulated examples and compared to a recently proposed alternative

efficient subspace based algorithm for adaptive bearing estimation and tracking in some practical applications of array processing the directions of the incident signals should be estimated adaptively and or the time varying directions should be tracked promptly in this paper an adaptive bearing estimation and tracking abest algorithm is investigated for estimating and tracking the uncorrelated and correlated narrow band signals impinging on a uniform linear array ula based on the subspace based method without eigendecomposition sumwe where a linear operator is obtained from the array data to form a basis for the null space by exploiting the array geometry and its shift invariance property specifically the null space is estimated using the least mean square lms or normalized lms nlms algorithm and the directions are updated using the approximate newton method the transient analyses of the lms and nlms algorithms are studied where the weight i e the linear operator is in the form of a matrix and there is a correlation between the additive noise and input data that involve the instantaneous correlations of the received array data in the updating equation and the step size stability conditions are derived explicitly in addition the analytical expressions for the mean square error mse and mean square deviation msd learning curves of the lms algorithm are clarified the effectiveness of the abest algorithm is verified and the theoretical analyses are corroborated through numerical examples simulation results show that the abest algorithm is computationally simple and has good adaptation and tracking abilities

wavelet based sequential monte carlo blind receivers in fading channels with unknown channel statistics recently an adaptive bayesian receiver for blind detection in flat fading channels was developed by the present authors based on the sequential monte carlo methodology that work is built on a parametric modeling of the fading process in the form of a state space model and assumes the knowledge of the second order statistics of the fading channel in this paper we develop a nonparametric approach to the problem of blind detection in fading channels without assuming any knowledge of the channel statistics the basic idea is to decompose the fading process using a wavelet basis and to use the sequential monte carlo technique to track both the wavelet coefficients and the transmitted symbols a novel resampling based wavelet shrinkage technique is proposed to dynamically choose the number of wavelet coefficients to best fit the fading process under such a framework blind detectors for both flat fading channels and frequency selective fading channels are developed simulation results are provided to demonstrate the excellent performance of the proposed blind adaptive receivers

fast principal component extraction by a weighted information criterion principal component analysis pca is an essential technique in data compression and feature extraction and there has been much interest in developing fast pica algorithms on the basis of the concepts of both weighted subspace and information maximization this paper proposes a weighted information criterion winc for searching the optimal solution of a linear neural network we analytically show that the optimum weights globally asymptotically converge to the principal eigenvectors of a stationary vector stochastic process we establish a dependent relation of choosing the weighting matrix on statistics of the input process through the analysis of stability of the equilibrium of the proposed criterion therefore we are able to reveal the constraint on the choice of a weighting matrix we develop two adaptive algorithms based on the winc for extracting in parallel multiple principal components both algorithms are able to provide adaptive step size which leads to a significant improvement in the learning performance furthermore the recursive least squares rls version of winc algorithms has a low computational complexity o np where n is the input vector dimension and p is the number of desired principal components in fact the winc algorithm corresponds to a three layer linear neural network model capable of performing in parallel the extraction of multiple principal components the winc algorithm also generalizes some well known pca psa algorithms just by adjusting the corresponding parameters since the weighting matrix does not require an accurate value it facilitates the system design of the winc algorithm for practical applications the accuracy and speed advantages of the winc algorithm are verified through simulations

particle filters for state estimation of jump markov linear systems jump markov linear systems jmls are linear systems whose parameters evolve with time according to a finite state markov chain in this paper our aim is to recursively compute optimal state estimates for this class of systems we present efficient simulation based algorithms called particle filters to solve the optimal filtering problem as well as the optimal fixed lag smoothing problem our algorithms combine sequential importance sampling a selection scheme and markov chain monte carlo methods they use several variance reduction methods to make the most of the statistical structure of jmls computer simulations are carried out to evaluate the performance of the proposed algorithms the problems of on line deconvolution of impulsive processes and of tracking a maneuvering target are considered it is shown that our algorithms outperform the current methods

a superposition principle for the kalman filter abstract in this paper decomposition formulas for the discrete time kalman filter are presented both the state estimate and the error covariance matrix are expressed as the sum of two terms the first being the estimate corresponding to zero initial conditions and the second being an explicit function of the initial values x 0 and p 0 the representation is updated in time by well behaved finite complexity matrix recursions and allows for a direct evaluation of the estimates for variable initial conditions applications to stochastic hybrid filtering are discussed

state estimation for discrete time markov jump linear systems with time correlated and mode dependent measurement noise abstract the state estimation problem for discrete time markov jump linear systems corrupted by time correlated and mode dependent measurement noise is considered where the time correlated and mode dependent measurement noise is described via a discrete time stochastic system with markov parameter and kronecker delta function by defining the measurement noise in this manner both time correlation and periodic step change caused by the change of system environment or structure can be embodied in the measurement noise a novel distributed measurement differencing method is applied to the problem of state estimation under consideration so that two algorithms are obtained using some results presented in this paper the first algorithm is optimal in the sense of minimum mean square error which can exactly compute the minimum mean square error estimate of system state the second algorithm is suboptimal and the suboptimality of the algorithm is caused by using some gaussian hypotheses the two proposed algorithms are recursive and the proposed suboptimal algorithm has a time independent complexity the performance of the proposed suboptimal algorithm is illustrated using computer simulations

brief paper detection and estimation for abruptly changing systems the problem of state estimation and system structure detection for discrete stochastic dynamical systems with parameters which may switch among a finite set of values is considered the switchings are modelled by a markov chain with known transition probabilities a brief survey and a unified treatment of the existing suboptimal algorithms are provided the optimal algorithms require exponentially increasing memory and computations with time simulation results comparing the various suboptimal algorithms are presented

brief paper risk sensitive filtering for jump markov linear systems in this paper a risk sensitive multiple model filtering algorithm is derived using the reference probability methods first the approximation of the interacting multiple model imm algorithm is identified in the reference probability domain then the same type of approximation is used to derive the finite dimensional risk sensitive filtering algorithm the derived algorithm reduces to the imm filter when the risk sensitive parameter goes to zero and reduces to the risk sensitive filter for linear gauss markov systems when the number of models is unity the algorithm performs better in a simulated uncertain parameter scenario than the imm filter

stochastic sampling algorithms for state estimation of jump markov linear systems jump markov linear systems are linear systems whose parameters evolve with time according to a finite state markov chain given a set of observations our aim is to estimate the states of the finite state markov chain and the continuous in space states of the linear system the computational cost in computing conditional mean or maximum a posteriori map state estimates of the markov chain or the state of the jump markov linear system grows exponentially in the number of observations we present three globally convergent algorithms based on stochastic sampling methods for state estimation of jump markov linear systems the cost per iteration is linear in the data length the first proposed algorithm is a data augmentation da scheme that yields conditional mean state estimates the second proposed scheme is a stochastic annealing sa version of da that computes the joint map sequence estimate of the finite and continuous states finally a metropolis hastings da scheme based on sa is designed to yield the map estimate of the finite state markov chain convergence results of the three above mentioned stochastic algorithms are obtained computer simulations are carried out to evaluate the performances of the proposed algorithms the problem of estimating a sparse signal developing from a neutron sensor based on a set of noisy data from a neutron sensor and the problem of narrow band interference suppression in spread spectrum code division multiple access cdma systems are considered

linear minimum mean square error estimation for discrete time markovian jump linear systems the linear minimum mean square error estimator lmmse for discrete time linear systems subject to abrupt changes in the parameters modeled by a markov chain spl theta k spl epsiv 1 n is considered the filter equations are derived from geometric arguments in a recursive form resulting in an on line algorithm suitable for computer implementation the author s approach is based on estimating x k 1 sub spl theta k i instead of estimating directly x k the uncertainty introduced by the markovian jumps increases the dimension of the filter to n n 1 where n is the dimension of the state variable an example where the dimension of the filter can be reduced to n is presented as well as a numerical comparison with the imm filter

dynamic conditional independence models and markov chain monte carlo methods abstract in dynamic statistical modeling situations observations arise sequentially causing the model to expand by progressive incorporation of new data items and new unknown parameters for example in clinical monitoring patients and data arrive sequentially and new patient specific parameters are introduced with each new patient markov chain monte carlo mcmc might be used for continuous updating of the evolving posterior distribution but would need to be restarted from scratch at each expansion stage thus mcmc methods are often too slow for real time inference in dynamic contexts by combining mcmc with importance resampling we show how real time sequential updating of posterior distributions can be effected the proposed dynamic sampling algorithms use posterior samples from previous updating stages and exploit conditional independence between groups of parameters to allow samples of parameters no longer of interest to be discarded such as when a patient dies or is discharged we apply the

convergence of learning algorithms with constant learning rates the behavior of neural network learning algorithms with a small constant learning rate epsilon in stationary random input environments is investigated it is rigorously established that the sequence of weight estimates can be approximated by a certain ordinary differential equation in the sense of weak convergence of random processes as epsilon tends to zero as applications backpropagation in feedforward architectures and some feature extraction algorithms are studied in more detail

robust m ary detection filters and smoothers for continuous time jump markov systems in this paper we consider a dynamic m ary detection problem when markov chains are observed through a wiener process these systems are fully specified by a candidate set of parameters whose elements are a rate matrix for the markov chain and a parameter for the observation model further we suppose these parameter sets can switch according to the state of an unobserved markov chain and thereby produce an observation process generated by time varying jump stochastic parameter sets given such an observation process and a specified collection of models we estimate the probabilities of each model parameter set explaining the observation by defining a new augmented state process then applying the method of reference probability we compute matrix valued dynamics whose solutions estimate joint probabilities for all combinations of candidate model parameter sets and values taken by the indirectly observed state process these matrix valued dynamics satisfy a stochastic integral equation with a wiener process integrator using the gauge transformation techniques introduced by clark and a pointwise matrix product we compute robust matrix valued dynamics for the joint probabilities on the augmented state space in these new dynamics the observation wiener process appears as a parameter matrix in a linear ordinary differential equation rather than an integrator in a stochastic integral equation it is shown that these robust dynamics when discretised enjoy a deterministic upper bound which ensures nonnegative probabilities for any observation sample path in contrast no such upper bounds can be computed for taylor expansion approximations such as the euler maryauana and milstein schemes finally by exploiting a duality between causal and anticausal robust detector dynamics we develop an algorithm to compute smoothed mode probability estimates without stochastic integrations a computer simulation demonstrating performance is included

sequential imputations and bayesian missing data problems abstract for missing data problems tanner and wong have described a data augmentation procedure that approximates the actual posterior distribution of the parameter vector by a mixture of complete data posteriors their method of constructing the complete data sets is closely related to the gibbs sampler both required iterations and similar to the em algorithm convergence can be slow we introduce in this article an alternative procedure that involves imputing the missing data sequentially and computing appropriate importance sampling weights in many applications this new procedure works very well without the need for iterations sensitivity analysis influence analysis and updating with new data can be performed cheaply bayesian prediction and model selection can also be incorporated examples taken from a wide range of applications are used for illustration

on line inference for hidden markov models via particle filters we consider the on line bayesian analysis of data by using a hidden markov model where inference is tractable conditional on the history of the state of the hidden component a new particle filter algorithm is introduced and shown to produce promising results when analysing data of this type the algorithm is similar to the mixture kalman filter but uses a different resampling algorithm we prove that this resampling algorithm is computationally efficient and optimal among unbiased resampling algorithms in terms of minimizing a squared error loss function in a practical example that of estimating break points from well log data our new particle filter outperforms two other particle filters one of which is the mixture kalman filter by between one and two orders of magnitude

asymptotic performance analysis of subspace adaptive algorithms introduced in the neural network literature in the neural network literature many algorithms have been proposed for estimating the eigenstructure of covariance matrices we first show that many of these algorithms when presented in a common framework show great similitudes with the gradient like stochastic algorithms usually encountered in the signal processing literature we derive the asymptotic distribution of these different recursive subspace estimators a closed form expression of the covariances in distribution of eigenvectors and associated projection matrix estimators are given and analyzed in particular closed form expressions of the mean square error of these estimators are given it is found that these covariance matrices have a structure very similar to those describing batch estimation techniques the accuracy of our asymptotic analysis is checked by numerical simulations and it is found to be valid not only for a small step size but in a very large domain finally convergence speed and deviation from orthonormality of the different algorithms are compared and several tradeoffs are analyzed

gaussian sum particle filtering we use the gaussian particle filter to build several types of gaussian sum particle filters these filters approximate the filtering and predictive distributions by weighted gaussian mixtures and are basically banks of gaussian particle filters then we extend the use of gaussian particle filters and gaussian sum particle filters to dynamic state space dss models with non gaussian noise with non gaussian noise approximated by gaussian mixtures the non gaussian noise models are approximated by banks of gaussian noise models and gaussian mixture filters are developed using algorithms developed for gaussian noise dss models as a result problems involving heavy tailed densities can be conveniently addressed simulations are presented to exhibit the application of the framework developed herein and the performance of the algorithms is examined

adaptive joint detection and estimation in mimo systems a hybrid systems approach an adaptive receiver based on hybrid system theory is developed for a multiuser multiple input multiple output mimo fading code division multiple access cdma system the basic idea is to treat the transmitted symbols and channel gains as unknown states discrete and continuous within a hybrid systems framework the bayesian inference based state estimation is derived using multiple model theory resulting in an optimal joint sequence estimator which is shown to be intractable in its computational complexity a suboptimal receiver imm sic is then derived based on the well known interacting multiple model imm algorithm and successive interference cancellation sic scheme this paper shows the specific approximations made to the probability densities of the optimal receiver in deriving the imm sic receiver with complexity linear in number of users this receiver design is well suited for online recursive processing of space time coded cdma system where the decoding stage is incorporated within the multiple model framework

exact hybrid filters in discrete time using the reference probability method and the change of measure in discrete time the state estimator problem is considered for linear systems observed in gaussian noise when the coefficients are functions of a noisily observed finite state markov chain the methods are new and finite dimensional filters are obtained however the number of statistics increases in time a numerical comparison of this filter with the interactive multiple model algorithm introduced by blom and bar shalom 1988 is given

state estimation for markovian jump linear systems with bounded disturbances in this paper we investigate the state estimation problem for a class of markovian jump linear systems mjlss in the presence of bounded polyhedral disturbances a set membership estimation algorithm is first proposed to find the smallest consistent set of all possible states which is shown to be expressed by a union of multiple polytopes the posterior probabilities of the system jumping modes are then estimated by introducing the lebesgue measure based on which the optimal point estimate is further provided moreover a similarity relationship for polytopes is defined and an approximate method is presented to calculate the minkowski sum of polytopes which can help reduce the computational complexity of the overall estimation algorithm

global analysis of oja s flow for neural networks a detailed study of oja s learning equation in neural networks is undertaken in this paper not only are such fundamental issues as existence uniqueness and representation of solutions completely resolved but also the convergence issue is resolved it is shown that the solution of oja s equation is exponentially convergent to an equilibrium from any initial value moreover the necessary and sufficient conditions are given on the initial value for the solution to converge to a dominant eigenspace of the associated autocorrelation matrix as a by product this result confirms one of oja s conjectures that the solution converges to the principal eigenspace from almost all initial values some other characteristics of the limiting solution are also revealed these facilitate the determination of the limiting solution in advance using only the initial information two examples are analyzed demonstrating the explicit dependence of the limiting solution on the initial value in another respect it is found that oja s equation is the gradient flow of generalized rayleigh quotients on a stiefel manifold

a blind particle filtering detector of signals transmitted over flat fading channels a new particle filtering detector pfd is proposed for blind signal detection over flat rayleigh fading channels whose model coefficients are unknown the detector employs a hybrid importance function and a mixture kalman filter it also incorporates an auxiliary particle filtering strategy with a smoothing kernel in the resampling step further by considering practical information of communication systems and the physical interpretation of the adopted second order autoregressive ar channel model a fully blind particle filtering implementation is developed the structure of the proposed pfd can be easily adapted to other system requirements simulations are provided that demonstrate the performance of the new pfd

particle filtering for partially observed gaussian state space models summary solving bayesian estimation problems where the posterior distribution evolves over time through the accumulation of data has many applications for dynamic models a large number of algorithms based on particle filtering methods also known as sequential monte carlo algorithms have recently been proposed to solve these problems we propose a special particle filtering method which uses random mixtures of normal distributions to represent the posterior distributions of partially observed gaussian state space models this algorithm is based on a marginalization idea for improving efficiency and can lead to substantial gains over standard algorithms it differs from previous algorithms which were only applicable to conditionally linear gaussian state space models computer simulations are carried out to evaluate the performance of the proposed algorithm for dynamic tobit and probit models

local momentum autoregression and the modeling of interest rate term structure abstract a parsimonious autoregressive model that is globally mean reverting but locally driven by momentum is proposed the local momentum autoregression lm ar model carries one extra parameter and depending on the sign of this extra parameter it can be either local momentum preserving or momentum building the lm ar model is motivated by observing us interest rate movement over many decades which over a long time span seems to mean revert but over a period of several months or years can actually exhibit a momentum like behavior we use the lm ar model with a stochastic central tendency factor as the dominant global risk factor in interest rates and add a local variation component of the standard mean reverting type to create a 3 factor risk environment we then derive its corresponding term structure model and empirically implement the model on us interest rates of seven maturities from january 1954 to december 2013 on a weekly frequency to establish the presence of local momentum building

bayesian conditional monte carlo algorithms for nonlinear time series state estimation bayesian filtering aims at estimating sequentially a hidden process from an observed one in particular sequential monte carlo smc techniques propagate in time weighted trajectories which represent the posterior probability density function pdf of the hidden process given the available observations on the other hand conditional monte carlo cmc is a variance reduction technique which replaces the estimator of a moment of interest by its conditional expectation given another variable in this paper we show that up to some adaptations one can make use of the time recursive nature of smc algorithms in order to propose natural temporal cmc estimators of some point estimates of the hidden process which outperform the associated crude monte carlo mc estimator whatever the number of samples we next show that our bayesian cmc estimators can be computed exactly or approximated efficiently in some hidden markov chain hmc models in some jump markov state space systems jmss as well as in multitarget filtering finally our algorithms are validated via simulations

conditional densities for continuous time nonlinear hybrid systems with applications to fault detection continuous time nonlinear stochastic differential state and measurement equations all of which have coefficients capable of abrupt changes at a random time are considered finite state jump markov chains are used to model the changes conditional probability densities which are essential in obtaining filtered estimates for these hybrid systems are then derived they are governed by a coupled system of stochastic partial differential equations when the q matrix of the markov chain is either lower or upper diagonal it is shown that the system of conditional density equations is finite dimensional computable these findings are then applied to a fault detection problem to compute state estimates that include the failure time

silverman algorithm and the structure of discrete time stochastic systems abstract it is a well known yet poorly understood fact that contrary to the continuous time case the same discrete time process y can be represented by minimal linear models see 1 1a 1 1b below which may either have a non singular or a singular d matrix in fact models with d 0 have been commonly used in the statistical literature on the other hand for models with a singular d matrix the riccati difference equation of kalman filtering involves in general the pseudo inversion of a singular matrix this cheap filtering problem dual to the better known cheap control problem has been studied for several decades in connection with the so called invariant directions of the riccati equation for a singular d a reduction of the order of the riccati equation is in general possible the reasons for such a reduction do not seem to be completely clear either in this paper we provide an explanation of this phenomenon from the classical point of view of zero flipping among minimal spectral factors changing d s occurs whenever zeros are flipped from z to their reciprocals at z 0 it is well known that for finite zeros the zero flipping process takes place by multiplication of the underlying spectral factor by a suitable rational all pass matrix function for infinite zeros zero flipping is implemented by a dual version of the silverman structure algorithm using this interpretation we derive a new algorithm for filtering of non regular processes based on a reduced order riccati equation we also obtain a precise characterization of the reduction of the order of the riccati equation which is afforded by zeros either at z or at the origin this order reduction has traditionally been associated with the study of invariant directions a point of view which as we show does not capture the essence of the phenomenon

filtering via simulation auxiliary particle filters this article analyses the recently suggested particle approach to filtering time series we suggest that the algorithm is not robust to outliers for two reasons the design of the simulators and the use of the discrete support to represent the sequentially updating prior distribution here we tackle the first of these problems

on hybrid state estimation for stochastic hybrid systems this paper considers the state estimation problem for the general continuous time stochastic hybrid system shs which has various applications defined on the hybrid state space the shs has the interacting discrete dynamics and continuous dynamics subject to various uncertainties the hybrid state estimation problem is to estimate both the continuous state and the discrete state of the shs with the information given by a continuous time observation process in this paper the hybrid state estimation problem is mathematically formulated and the corresponding filtering equations that are stochastic partial differential equations are derived to describe the evolution of the hybrid state estimates conditioned on the observation history a numerical algorithm based on a finite difference approach is proposed to solve the filtering equations a markov chain mc is constructed on the descretized hybrid state space to approximate the infinitesimal generator of the shs and then hybrid state estimation for the shs is reduced to estimating the state of the mc it is proved that the state estimation results of the mc converge to the solution to the filtering equations as the constructed mc converges to the shs an illustrative example of aircraft tracking is used to demonstrate the performance of the proposed algorithm

the calculation of posterior distributions by data augmentation comment a noniterative sampling importance resampling alternative to the data augmentation algorithm for creating a few imputations when fractions of missing information are modest the sir algorithm

some aspects of variational inequalities abstract in this paper we provide an account of some of the fundamental aspects of variational inequalities with major emphasis on the theory of existence uniqueness computational properties various generalizations sensitivity analysis and their applications we also propose some open problems with sufficient information and references so that someone may attempt solution s in his her area of special interest we also include some new results which we have recently obtained

discrete time estimation of a markov chain with marked point process observations application to markovian jump filtering in this paper various discrete time estimation problems are studied for a finite and homogeneous markov chain observed by a marked point process these problems which could have significant applications in target tracking manufacturing or communication theory have never been studied in the literature the quantities to be estimated are the state the number of jumps and the occupation times the identification of the chain transition matrix is also addressed via an expectation maximization procedure solutions in the sense of the conditional distribution are obtained by a change of probability measure and are shown to have convenient recursive forms the efficiency of this new approach for sensor modeling is illustrated by the study of a linear markovian jump filtering problem where in addition to a classical state observation a mode markov point process observation is assumed a numerical example is given

on stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix abstract in applications of signal processing and pattern recognition eigenvectors and eigenvalues of the statistical mean of a random matrix sequence are needed iterative methods are suggested and analyzed in which no sample moments are used convergence is shown by stochastic approximation theory

performance analysis of an adaptive algorithm for tracking dominant subspaces this paper provides a performance analysis of a least mean square lms dominant invariant subspace algorithm based on an unconstrained minimization problem this algorithm is a stochastic gradient algorithm driving the columns of a matrix w to an orthonormal basis of a dominant invariant subspace of a correlation matrix we consider the stochastic algorithm governing the evolution of ww sup h to the projection matrix onto this dominant invariant subspace and study its asymptotic distribution a closed form expression of its asymptotic covariance is given in the case of independent observations and is further analyzed to provide some insights into the behavior of this lms type algorithm in particular it is shown that even though the algorithm does not constrain w to have orthonormal columns there is deviation from orthonormality of the first order we also give a closed form expression of the asymptotic covariance of the doas estimated by the music algorithm applied to the adaptive estimate of the projector it is found that the asymptotic distributions have a structure that is very similar to those describing batch estimation techniques because both algorithms are obtained from the minimization of the same criterion finally the accuracy of the asymptotic analysis is checked by numerical simulations and is found to be valid not only for a small step size but in a very large domain

asymptotic distributions associated to oja s learning equation for neural networks we perform a complete asymptotic performance analysis of the stochastic approximation algorithm denoted subspace network learning algorithm derived from oja s learning equation in the case where the learning rate is constant and a large number of patterns is available this algorithm drives the connection weight matrix w to an orthonormal basis of a dominant invariant subspace of a covariance matrix our approach consists in associating to this algorithm a second stochastic approximation algorithm that governs the evolution of ww sup t to the projection matrix onto this dominant invariant subspace then using a general result of gaussian approximation theory we derive the asymptotic distribution of the estimated projection matrix closed form expressions of the asymptotic covariance of the projection matrix estimated by the snl algorithm and by the smoothed snl algorithm that we introduce are given in the case of independent or correlated learning patterns and are further analyzed it is found that the structures of these asymptotic covariance matrices are similar to those describing batch estimation techniques the accuracy or our asymptotic analysis is checked by numerical simulations and it is found to be valid not only for a small learning rate but in a very large domain finally improvements brought by our smoothed snl algorithm are shown such as the learning speed misadjustment tradeoff and the deviation from orthonormality

image enhanced multiple model tracking we consider tracking algorithms for manoeuvring targets when the observations include extra information on the current operating mode of the target obtained from an image sensor the target is modelled as a markov jump linear system and the image based observations form a discrete time point process we derive the optimal minimum mean square error filtered estimate which intrinsically fuses the image based and primary observations this optimal filter is computationally prohibitive but provides the basis for a clear understanding of various suboptimal approaches we propose the image enhanced imm filter as a practical alternative which retains many desirable properties of the optimal filter and outperforms existing image enhanced tracking algorithms over a broad range of operating scenarios

kalman filtering for linear systems with coefficients driven by a hidden markov jump process abstract we present an explicit algorithm for the solution of the nonlinear filtering problem concerning a linear partially observed diffusion type model where the coefficients are driven by a hidden markov jump process of which one can observe the occurrence of a jump the recursive filter is of a branching type with its finite dimension growing for each jump of the hidden markov process

particle filter with a mode tracker for visual tracking across illumination changes in this correspondence our goal is to develop a visual tracking algorithm that is able to track moving objects in the presence of illumination variations in the scene and that is robust to occlusions we treat the illumination and motion x y translation and scale parameters as the unknown state sequence the observation is the entire image and the observation model allows for occasional occlusions modeled as outliers the nonlinearity and multimodality of the observation model necessitate the use of a particle filter pf due to the inclusion of illumination parameters the state dimension increases thus making regular pfs impractically expensive we show that the recently proposed approach using a pf with a mode tracker can be used here since even in most occlusion cases the posterior of illumination conditioned on motion and the previous state is unimodal and quite narrow the key idea is to importance sample on the motion states while approximating importance sampling by posterior mode tracking for estimating illumination experiments demonstrate the advantage of the proposed algorithm over existing pf based approaches for various face and vehicle tracking we are also able to detect illumination model changes e g those due to transition from shadow to sunlight or vice versa by using the generalized expected log likelihood statistics and successfully compensate for it without ever loosing track

analysis of gradient algorithms for tls based adaptive iir filters steepest descent gradient algorithms for unbiased equation error adaptive infinite impulse response iir filtering are analyzed collectively for both the total least squares and mixed least squares total least squares framework these algorithms have a monic normalization that allows for a direct filtering implementation we show that the algorithms converge to the desired filter coefficient vector we achieve the convergence result by analyzing the stability of the equilibrium points and demonstrate that only the desired solution is locally stable additionally we describe a region of initialization under which the algorithm converges to the desired solution we derive the results using interlacing relationships between the eigenvalues of the data correlation matrices and their respective schur complements finally we illustrate the performance of these new approaches through simulation

blind deconvolution via sequential imputations abstract the sequential imputation procedure is applied to adaptively and sequentially reconstruct discrete input signals that are blurred by an unknown linear moving average channel and contaminated by additive gaussian noises a problem known as blind deconvolution in digital communication a rejuvenation procedure for improving the efficiency of sequential imputation is introduced and theoretically justified the proposed method does not require the channel to be nonminimum phase and can be used in real time signal restoration two simulated systems are studied to illustrate the proposed method our result shows that the ideas of multiple imputations and flexible simulation techniques are as powerful in engineering as in survey sampling

covariance structure of the gibbs sampler with applications to the comparisons of estimators and augmentation schemes summary we study the covariance structure of a markov chain generated by the gibbs sampler with emphasis on data augmentation when applied to a bayesian missing data problem the gibbs sampler produces two natural approximations for the posterior distribution of the parameter vector the empirical distribution based on the sampled values of the parameter vector and a mixture of complete data posteriors we prove that rao blackwellization causes a one lag delay for the autocovariances among dependent samples obtained from data augmentation and consequently the mixture approximation produces estimates with smaller variances than the empirical approximation the covariance structure results are used to compare different augmentation schemes it is shown that collapsing and grouping random components in a gibbs sampler with two or three components usually result in more efficient sampling schemes

inference and model choice for sequentially ordered hidden markov models summary the system equation of a hidden markov model is rewritten to label the components by order of appearance and to make explicit the random behaviour of the number of components mt we argue that this reformulation is often a good way to achieve identifiability as it facilitates the interpretation of the posterior density and the estimation of the number of components that have appeared in a given sample we develop a sequential monte carlo algorithm for estimating the reformulated model which relies on particle filtering and gibbs sampling our algorithm has a computational cost that is similar to that of a markov chain monte carlo sampler and is much less likely to be affected by label switching i e the possibility of becoming trapped in a local mode of the posterior density the extension to transdimensional priors is also considered the approach is illustrated by two real data examples

exact bayesian filter and joint imm coupled pda tracking of maneuvering targets from possibly missing and false measurements this paper represents the problem of tracking multiple maneuvering targets from possibly missing and false measurements as one of filtering for a jump linear descriptor system with stochastic i i d coefficients this particular representation serves as an instrument in the characterization of the exact bayesian filter subsequently novel finite dimensional filter algorithms are developed through introducing approximations to the exact bayesian solution one filter approximation assumes conditionally gaussian density of the joint target state given the joint target maneuver mode and the algorithm is referred to as joint imm coupled pda jimmcpda the specialty of this filter algorithm is that both the imm step and the pda step are performed jointly over the modes and states of all targets subsequently the cpda track coalescence avoiding hypothesis pruning approach of blom bloem 2000 probabilistic data association avoiding track coalescence ieee transactions of automatic control 45 247 259 is extended to bring the joint target modes into account the resulting filter algorithm is referred to as track coalescence avoiding joint imm coupled pda the two novel algorithms are compared to immjpda and immpda through monte carlo simulations

mixture kalman filters in treating dynamic systems sequential monte carlo methods use discrete samples to represent a complicated probability distribution and use rejection sampling importance sampling and weighted resampling to complete the on line filtering task we propose a special sequential monte carlo method the mixture kalman filter which uses a random mixture of the gaussian distributions to approximate a target distribution it is designed for on line estimation and prediction of conditional and partial conditional dynamic linear models which are themselves a class of widely used non linear systems and also serve to approximate many others compared with a few available filtering methods including monte carlo methods the gain in efficiency that is provided by the mixture kalman filter can be very substantial another contribution of the paper is the formulation of many non linear systems into conditional or partial conditional linear form to which the mixture kalman filter can be applied examples in target tracking and digital communications are given to demonstrate the procedures proposed

a low complexity sequential monte carlo algorithm for blind detection in mimo systems in statistical signal processing the sequential monte carlo smc method is powerful and can approach the theoretical optima however its computational complexity is usually very high especially in multiple input multiple output mimo systems this paper presents a new low complexity smc lc smc algorithm for blind detection in mimo systems the main idea of which is to shrink the sampling space via channel estimation which is initialized using the first differentially modulated symbol and then updated using the monte carlo samples since the a posteriori probability of the transmitted symbols can be calculated separately by each transmit antenna the proposed lc smc algorithm is not only computationally efficient as compared to the original smc whose complexity grows exponentially with the number of transmit antennas but also makes blind turbo receiver more feasible for multilevel phase modulations simulation results are presented to demonstrate the effectiveness of the lc smc algorithm

projection approximation subspace tracking subspace estimation plays an important role in a variety of modern signal processing applications we present a new approach for tracking the signal subspace recursively it is based on a novel interpretation of the signal subspace as the solution of a projection like unconstrained minimization problem we show that recursive least squares techniques can be applied to solve this problem by making an appropriate projection approximation the resulting algorithms have a computational complexity of o nr where n is the input vector dimension and r is the number of desired eigencomponents simulation results demonstrate that the tracking capability of these algorithms is similar to and in some cases more robust than the computationally expensive batch eigenvalue decomposition relations of the new algorithms to other subspace tracking methods and numerical issues are also discussed