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Specifically, such an estimation process is achieved by using a set of spatially distributed mobile sensors. The objective is to provide mobile sensor control policies that aim to improve the state estimate. The metric for such an estimate improvement is taken to be the expected state estimation error. Using different spatial norms, two guidance policies are proposed. The current approach capitalizes on the efficient filter gain design in order to avoid intense computational requirements resulting from the solution to filter Riccati equations. Simulation studies implementing and comparing the two proposed control policies are provided.<\/jats:p>","DOI":"10.1137\/060677884","type":"journal-article","created":{"date-parts":[[2009,2,11]],"date-time":"2009-02-11T23:04:59Z","timestamp":1234393499000},"page":"266-291","source":"Crossref","is-referenced-by-count":89,"title":["Estimation of Spatially Distributed Processes Using Mobile Spatially Distributed Sensor Network"],"prefix":"10.1137","volume":"48","author":[{"given":"Michael A.","family":"Demetriou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Islam I.","family":"Hussein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"351","published-online":{"date-parts":[[2009,2,11]]},"reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.1177\/027836402320556368"},{"key":"R2","unstructured":"R. A. 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