Kalman Filter techniques

Grimble, M.J., Patton, R.J. and Wise, D.A. (1979) The design of dynamic ship positioning control systems using extended Kalman filtering techniques, IEEE Conference, Oceans 79, CA, San Diego. 

As was previously shown, Kalman filtering techniques can be, and have been, successfully used on dynamic process data, to smooth measurement data recursively and... 

Note that the proposed check must be perfomed after having obtained the estimation of u. In contrast, in the Kalman filter technique (jt), the corresponding values of e and Eg may be recursively calculated along with the input estimate. 

R. Pachter, R. B. Altman, and O. Jardetzky, /. Magn. Reson., 89, 578 (1990). The Dependence of a Protein Solution Structure on the Quality of the Input NMR Data. Application of the Double-Iterated Kalman Filter Technique to Oxytocin. 

The effects of autocorrelation on monitoring charts have also been reported by other researchers for Shewhart [186] and CUSUM [343, 6] charts. Modification of the control limits of monitoring charts by assuming that the process can be represented by an autoregressive time series model (see Section 4.4 for terminology) of order 1 or 2, and use of recursive Kalman filter techniques for eliminating autocorrelation from process data have also been proposed... 

To summarize, we propose a so-called MMSE forecast adaptive base-stock policy. This policy employs the Kalman filter technique to calculate minimum mean square error (MMSE) forecasts of future demands at the beginning of each period. A fixed safety stock 7 set at the beginning of the planning horizon, is then added to the MMSE forecast to form the target level /3t for this period. Then, the following rule is applied if the current inventory position is lower than the target level, an order is placed to fill this gap otherwise, no order is placed. The advantage of our policy is that it is intuitive and easily implementable. But, not less importantly, it can be tailored for use in information-rich supply chains, for which the characterization of optimal policies is virtually impossible. 

For linear systems, there are well-developed schemes to achieve robustness using active approach, e.g., Kalman filter and extended Kalman filter techniques. In those... 

Muon reconstruction, after local-pattern recognition is performed in two stages stand-alone reconstruction based on information from the muon system only and global reconstmction including the hit information of the silicon tracker. Standalone reconstruction starts from track segments in the muon chambers and muon trajectories are built from the inside to the outside using the Kalman filter technique. After the trajectory is built, a second Kalman filter, working from outside in, is applied to determine the track parameters. In the end, the track is extrapolated to the nominal interaction point and a vertex-constrained fit of the track parameters is performed. 

A new approach to design instrumentation networks for dynamic systems based on the Kalman filtering techniques is presented. Different performance measures are proposed and compared through a Case Study. 

Historically, treatment of measurement noise has been addressed through two distinct avenues. For steady-state data and processes, Kuehn and Davidson (1961) presented the seminal paper describing the data reconciliation problem based on least squares optimization. For dynamic data and processes, Kalman filtering (Gelb, 1974) has been successfully used to recursively smooth measurement data and estimate parameters. Both techniques were developed for linear systems and weighted least squares objective functions. 

The proposed technique is based on an extension to time-varying systems of Wiener s optimal filtering method (l-3). The estimation of the corrected chromato gram is optimal in the sense of minimizing the estimation error variance. A test for verifying the results is proposed, which is based on a comparison between the "innovations" sequence and its corresponding expected standard deviation. The technique is tested on both synthetic and experimental examples, and compared with an available recursive algorithm based on the Kalman filter ( ). 

In this work, an inverse filtering technique based on Wiener s optimal theory (1-3) is presented. This approach is valid for time-varying systems, and is solved in the time domain in mtrix form. Also, it is in many respects equivalent to the numerically "effl- lent" Kalman filtering approach described in ( ). For this reason, a... 

The proposed technique is numerically "robust", and its results are comparable to those obtained through a recursive method based on the Kalman filter ( L). It should be noted that because the present technique utilizes all of the information simultaneously, the results have been compared to those of the optimal smoother estimates in (1 ), which are "better" than the true filtered estimates. 

In order to perform the on-line optimization strategy, the knowledge of current state variables and/or parameters in the process models is required. Due to the fact that some of these variables cannot be known exactly or sometime can be measured with time delay, it is essential to include an on-line estimator to estimate these process variables using available process measurements as well. The sequence of an estimation and optimization procedure is known as an estimation-optimization task [6], As in several estimation techniques, an Extended Kalman Filter (EKF) has become increasingly popular because it is relatively easy to implement. It has been found that the EKF can be applied to a number of chemical process applications with great success. Once the estimate of unknown process variables is deter-... 

Although there is a close relationship among the various quantitative model-based techniques, observer-based approaches have become very important and diffused, especially within the automatic control community. Luenberger observers [1,45, 53], unknown input observers [44], and Extended Kalman Filters [21] have been mostly used in fault detection and identification for chemical processes and plants. Reviews of several model-based techniques for FD can be found in [8, 13, 35, 50] and, as for the observer-based methods, in [1, 36,44],... 

One alternative to the direct online measurement of polymer properties is to use a process model in conjunction with optimal state estimation techniques to predict the polymer properties. Indeed, several online state estimation techniques such as Kalman filters, nonlinear extended Kalman filters (EKF), and observers have been developed and applied to polymerization process systems. ° In implementing the online state estimator, several issues arise. For example, the standard filtering algorithm needs to be modified to accommodate time-delayed offline measurements (e.g., MWD, composition, conversion). The estimation update frequency needs to be optimally selected to compensate for the model inaccuracy. Table 5 shows the extended Kalman filter algorithm with delayed offline measurements. Fig. 2 illustrates the use of online state estimator... 

To include the information about process d3mamics in the models, the data matrix can be augmented with lagged values of data vectors, or model identification techniques such as subspace state-space modeling can be used (Section 4.5). Negiz and Cinar [209] have proposed the use of state variables developed with canonical variates based realization to implement SPM to multivariable continuous processes. Another approach is based on the use of Kalman filter residuals [326]. MSPM with dynamic process models is discussed in Section 5.3. The last section (Section 5.4) of the chapter gives a brief survey of other approaches proposed for MSPM. 

Several stochastic models, based on mutli-parametric regression, artificial neural networks, Kalman filter and other statistical techniques, were implemented for short-term forecast of air pollution episodes, namely high ozone concentrations (Czech Republic, Hungary, Poland, Slovenia). 

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