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The system is nonlinear and is approximated by a piecewise-linear dynamic model. The Box\u2013Jenkins model is an augmented model of the signal and the disturbance, is non-controllable and observable, while the signal model is controllable and observable. An emulator-based two-stage identification is employed to obtain an accurate system model needed to design the robust controller. The system and its KF are identified, and the signal and output errors are estimated. From the identified models, the signal, its KF, the disturbance model, and the whitening filter are all obtained using balanced model reduction techniques. It is shown that the signal model is a transfer matrix description relating the system output and KF residual, and the residual is the whitened output error. The disturbance model is identified by inverse filtering. A new combined feedforward\u2013feedback controller is designed and implemented using an internal model of the reference driven by both the error between the reference and the signal estimate, and by the feedforwarded reference signal. The proposed scheme was successfully evaluated on a simulated autonomously guided drone.<\/jats:p>","DOI":"10.1177\/01423312221142119","type":"journal-article","created":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T06:59:21Z","timestamp":1674543561000},"page":"1539-1557","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Tracking the trajectory of an object in a noisy environment with unknown statistics: A novel robust Kalman filter residue-based approach"],"prefix":"10.1177","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8278-183X","authenticated-orcid":false,"given":"Lahouari","family":"Cheded","sequence":"first","affiliation":[{"name":"Independent Researcher and Consultant, Life SMIEEE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajamani","family":"Doraiswami","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of New Brunswick, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2023,1,24]]},"reference":[{"issue":"1","key":"bibr1-01423312221142119","first-page":"1000126","volume":"4","author":"Ahmed EA","year":"2015","journal-title":"Advances in Robotics and Automation"},{"key":"bibr2-01423312221142119","doi-asserted-by":"publisher","DOI":"10.12988\/ams.2013.37385"},{"key":"bibr3-01423312221142119","volume-title":"Estimation and Tracking: Principles, Techniques, and Software","author":"Bar-Shalom Y","year":"1998"},{"key":"bibr4-01423312221142119","unstructured":"Cheded L, Doraiswami R (2015) A unified approach to detection and isolation of faults using a Kalman filter residual-based approach. 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