This paper introduces a new estimation approach for determining the sequence of internal manipulator configurations which are required to perform a task on an arbitrarily positioned and oriented workpiece, in the context of the method of camera-space manipulation— a robust and precise means of controlling three-dimensional robot maneuvers using vision. Despite a nonlinear estimation model, a re...

We develop a recursive estimator that systematically arrives at sparse parameter estimates. The algorithm is computationally feasible for moderate parameter estimation problems and leverages the Gaussian sum filter to provide both sparse parameter estimates and credible Bayesian intervals for non-zero parameters in a recursive fashion. Simulations show extremely promising accuracy, as well as a...

Combining the features of block matching algorithm and block recursive algorithm a new motion estimation method is proposed for complex motion phenomenon. With the full searching procedure used in block matching algorithm and the adaptive searching procedure used in block recursive algorithm, the proposed combinative algorithm can be used to estimate more complex motion parameters such as trans...

This paper studies recursive optimal filtering as well as robust fault and state estimation for linear stochastic systems with unknown disturbances. It proposes a new recursive optimal filter structure with transformation of the original system. This transformation is based on the singular value decomposition of the direct feedthrough matrix distribution of the fault which is assumed to be of a...

A Probabilistic Recursive Transition Network is an elevated version of a Recursive Transition Network used to model and process context-free languages in stochastic parameters. We present a re-estimation algorithm for training probabilistic parameters, and show how efficiently it can be implemented using charts. The complexity of the Outside algorithm we present is O(N4G 3) where N is the input...

The authors develop a self-contained theory for linear estimation in Krein spaces. The derivation is based on simple concepts such as projections and matrix factorizations and leads to an interesting connection between Krein space projection and the recursive computation of the stationary points of certain second-order (or quadratic) forms. The authors use the innovations process to obtain a ge...

This paper focuses on the use of parameter estimation techniques for the implementation of real-time Fault Detection and Diagnosis schemes. A detailed analysis of the nonrecursive and recursive Least Squares methods is given in the context of the system diagnosis problem, and a procedure for performing fault detection and identification for multivariable systems is proposed. An application exam...

We develop a Recursive L1-Regularized Least Squares (SPARLS) algorithm for the estimation of a sparse tap-weight vector in the adaptive filtering setting. The SPARLS algorithm exploits noisy observations of the tap-weight vector output stream and produces its estimate using an ExpectationMaximization type algorithm. Simulation studies in the context of channel estimation, employing multipath wi...

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In Earth observations problems, we usually have the situation of knowing accurately the probability density functions of the several classes of interest, and we need to classify a set of observations with unknown class proportions. The observations are in n-dimensional space, where n is the number of spectral bands. Two recursive algorithms for classifying the observations and estimating the pr...