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A number of different methods have been proposed, and we develop a conceptual framework to perform a comparative study of four different hybrid state estimation algorithms in this paper: (1) the switched extended Kalman filter; (2) focused hybrid estimation; (3) multiple-modal particle filtering; and (4) the one-step look-ahead particle filtering. The conceptual comparison is followed by an empirical evaluation, where we study the effectiveness of these algorithms in tracking the behaviors of a Reverse Osmosis Subsystem of an Advanced Water Recovery System that was developed at the NASA Johnson Space Center. We discuss our results, which show the strengths and weaknesses of each of these algorithms, and propose topics for further research into this important problem.<\/jats:p>","DOI":"10.1177\/0037549716638425","type":"journal-article","created":{"date-parts":[[2016,4,1]],"date-time":"2016-04-01T20:58:33Z","timestamp":1459544313000},"page":"357-376","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Comparison of state estimation techniques for nonlinear hybrid systems"],"prefix":"10.1177","volume":"92","author":[{"given":"Gan","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gautam","family":"Biswas","sequence":"additional","affiliation":[{"name":"Institute for Software Integrated Systems, Department of Electrical Engineering and Computer Science, Vanderbilt 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