this paper presents a methodology for design of instrumentation sensor networks in non-linear chemical plants. the method utilizes a robust extended kalman filter approach to provide an efficient dynamic data reconciliation. a weighted objective function has been introduced to enable the designer to incorporate each individual process variable with its own operational importance. to enhance the...

This is a companion paper of Crisan et el [4]. In this article, we study a few classes of solvable models of the stochastic filtering problems with Ornstein-Uhlenbeck noise: Firstly, we study the singular linear filter with OU noise. Secondly, for nonsingular linear filtering with OU noise, we consider the limit to the classical Kalman-Bucy filter as the OU process converges to the Brownian mot...

We extend the Kalman–Bucy filter to the case where both the system and observation processes are driven byfinite dimensional Lévy processes, butwhereas the process driving the system dynamics is square-integrable, that driving the observations is not; however it remains integrable. The main result is that the components of the observation noise that have infinite variance make no contribution t...

We examine the question of determining the "best" linear filter, in an expected squared error sense, for a signal generated by stochastic linear differential equation on a Hilbert space. Our results, which extend the development in Kalman and Bucy (1960), rely heavily on the integration theory for Banach-space-valued functions of Dunford and Schwartz (1958). In order to derive the Kalman-Bucy f...

Kalman-Bucy smoothers are often used to estimate the state variables as a function of time in a system with stochastic dynamics and measurement noise. This is accomplished using an algorithm for which the number of numerical operations grows linearly with the number of time points. All of the randomness in the model is assumed to be Gaussian. Including other available information, for example a...

This paper presents a set of filters with globally exponentially stable error dynamics for source localization and navigation, in 3-D, based on directionmeasurements from the agent (or vehicle) to the source, in addition to relative velocity readings of the agent. Both the source and the agent are allowed to have constant unknown drift velocities and the relative drift velocity is also explicit...

This paper presents the detection of 3D pose of moving humans heads. The contribution of this work is the conjunction of an e ective preprocessing method and a strong estimation Kalman technique which uses less noise sensitive 3D line observations. The procedure involves the detection of the target face in any type of background using color histograms. To outline the face we use a minimization ...

Abstract This paper builds on the filtering work presented in [1]. Our hybrid-state extended Kalman filter [1] is suitable for track-level processing [2] to maintain track through move-stop-move motion patterns, but is inadequate for processing report-level data. Similarly, a direct IMM approach to move-stopmove filtering appears inadequate, since it may lead to excessive track breaks and to ov...

This paper describes a new approach for generalizing the Kalman filter to nonlinear systems. A set of samples are used to parameterize the mean and covariance of a (not necessarily Gaussian) probability distribution. The method yields a filter that is more accurate than an extended Kalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter. Its effectiveness is demon...