Sampled-data Minimum Variance Filtering
نویسندگان
چکیده
This paper deals with the optimal solution to the sampled-data minimum variance filtering problem for linear systems with noise in the states and in the measurements. The solution is derived in the time-domain by using a fast sampling zeroorder hold input discretization of the continuous time systems together with a lifting technique. The original sampled-data system is transformed into an equivalent LTI discretetime system with infinite-dimensional inputoutput space. However, the designed filter is finite-dimensional. We derive both the existence conditions and the explicit expression of the desired filter and provide an illustrative numerical example. Keywords— Filtering, lifting, T -periodic systems, sampled-data.
منابع مشابه
A Stock Market Filtering Model Based on Minimum Spanning Tree in Financial Networks
There have been several efforts in the literature to extract as much information as possible from the financial networks. Most of the research has been concerned about the hierarchical structures, clustering, topology and also the behavior of the market network; but not a notable work on the network filtration exists. This paper proposes a stock market filtering model using the correlation - ba...
متن کاملStatistical Mapping of Cortical Activities using Minimum-Variance Maximum-Discrimination Spatial Filtering
This paper presents a new spatial filtering technique for statistical mapping of neuronal sources by using magnetoencephalography data. In addition to the unit-gain constraint and the minimum-variance criterion that can reconstruct the activation magnitude of the targeted source while suppressing the contribution from other sources, the proposed technique exploits a maximum-discrimination crite...
متن کاملHow to Estimate Change from Samples
Measurements, snapshots of a system, traffic matrices, and activity logs are typically collected repeatedly. Difference queries are then used to detect and localize changes for anomaly detection, monitoring, and planning. When the data is sampled, as is often done to meet resource constraints, queries are processed over the sampled data. We are not aware, however, of previously known estimators...
متن کاملLimitations of state estimation: absolute lower bound of minimum variance estimation/filtering, Gaussianity-whiteness measure (joint Shannon-Wiener entropy), and Gaussianing-whitening filter (maximum Gaussianity-whiteness measure principle)
This paper aims at obtaining performance limitations of state estimation in terms of variance minimization (minimum variance estimation and filtering) using information theory. Two new notions, negentropy rate and Gaussianity-whiteness measure (joint Shannon-Wiener entropy), are proposed to facilitate the analysis. Topics such as Gaussianing-whitening filter (the maximum Gaussianity-whiteness m...
متن کاملA Comparison of Seismic Array Processing Schemes
It is our purpose in this note to discuss three of the many approaches to seismic array processing from the theoretical point of view. The three are: 1) maximumlikelihood processing, 2) the minimum-variance, unbiased estimator (MVU) approach used by Levin, and 3) multichannel Wiener filtering. A feature common to these techniques is the formation of a single output waveform which serves as an e...
متن کامل