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2021 | OriginalPaper | Buchkapitel

42. An Unscented Kalman Filter Approach for High-Precision Indoor Localization

verfasst von : Yashwant Yerra, D. Ram Kumar Reddy, P. Sudheesh

Erschienen in: Intelligent Manufacturing and Energy Sustainability

Verlag: Springer Singapore

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Abstract

An indoor localized system is a network of sensors used to track people or objects that cannot be tracked completely using GPS in areas, such as multi-story buildings, airports, tunnels, and many more. A wide variety of techniques and technologies are implemented to provide indoor positioning ranging from already installed reconfigured devices such as smart phones, Wi-Fi and Bluetooth antennas, digital cameras, and clocks to specifically designed installations with strategically placed relays and beacons throughout a specified space. IPS has broad applications in the industrial, defense, retail, and inventory monitoring industries. In this paper, we implement the localization concept which receives a phased-radio signal which is fed into unscented Kalman filter (UKF) without any prior processing to subjugate. For this process, standard preprocessing concepts, like angle-of-arrival prediction, beam forming, and time-of-flight or time-difference-of-arrival predictions were never necessary. It completely nullifies the essential difficulties of other phase related, highly accurate techniques of localization similar to synthetic aperture methods. To prove this above procedure, let us employ a desirable setup with 24 Ghz frequency-modulated continuous-wave single-input-multiple-output (S.I.M.O) along with a secondary radar with bandwidth of 250 MHz. The ideal trajectory of the transmitter is assumed as helical in nature. Apart from the challenging conditions like interference due to noise and low bandwidth, the results produce good localization with a minimum RMSE of around a few centimeters. The suggested process could be utilized for almost any form of C.W-carrier E.M signal and provides an interesting unorthodox to traditional multi-purpose approaches.

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Literatur
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Zurück zum Zitat P. Sudheesh, M. Jayakumar, Nonlinear signal processing applications of variants of particle filter: a survey, in Lecture Notes in Electrical Engineering (accepted for publication), vol. 521 (2017), pp. 91–97 P. Sudheesh, M. Jayakumar, Nonlinear signal processing applications of variants of particle filter: a survey, in Lecture Notes in Electrical Engineering (accepted for publication), vol. 521 (2017), pp. 91–97
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Zurück zum Zitat J. Ramnarayan, J.P. Anita, P. Sudheesh, Estimation and tracking of a ballistic target using sequential importance sampling method. Commun. Comput. Inf. Sci. 746, 387–398 (2017) J. Ramnarayan, J.P. Anita, P. Sudheesh, Estimation and tracking of a ballistic target using sequential importance sampling method. Commun. Comput. Inf. Sci. 746, 387–398 (2017)
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Zurück zum Zitat H.F. Khazraj, F. da Silva, C.L. Bak, A performance comparison between extended Kalman filter and unscented Kalman filter in power system dynamic state estimation,in 2016 51st International Universities Power Engineering Conference (UPEC) (IEEE, 2016), pp. 1–6 H.F. Khazraj, F. da Silva, C.L. Bak, A performance comparison between extended Kalman filter and unscented Kalman filter in power system dynamic state estimation,in 2016 51st International Universities Power Engineering Conference (UPEC) (IEEE, 2016), pp. 1–6
6.
Zurück zum Zitat R.P. Tripathi, S. Ghosh, J.O. Chandle,Tracking of object using optimal adaptive Kalman filter, in 2016 IEEE International Conference on Engineering and Technology (ICETECH) (Coimbatore, 2016), pp. 1128–1131 R.P. Tripathi, S. Ghosh, J.O. Chandle,Tracking of object using optimal adaptive Kalman filter, in 2016 IEEE International Conference on Engineering and Technology (ICETECH) (Coimbatore, 2016), pp. 1128–1131
Metadaten
Titel
An Unscented Kalman Filter Approach for High-Precision Indoor Localization
verfasst von
Yashwant Yerra
D. Ram Kumar Reddy
P. Sudheesh
Copyright-Jahr
2021
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-33-4443-3_42

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