@InProceedings{7362717,
  author = {L. Chen and H. Kuusniemi and Y. Chen and J. Liu and L. Pei and L. Ruotsalainen and R. Chen},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {Constraint Kalman filter for indoor bluetooth localization},
  year = {2015},
  pages = {1915-1919},
  abstract = {This paper studies sequential estimation of indoor localization based on fingerprints of received signal strength indicators (RSSI). Due to the lack of an analytic formula for the fingerprinting measurements, the Kalman filter can not be directly applied. By introducing a hidden variable to represent the unknown positioning coordinate, a state model is formulated and a constrained Kalman filter (CKF) is then derived within the Bayesian framework. The update of the state incorporates the prior information of the motion model and the statistical property of the hidden variable estimated from the RSSI measurements. The positioning accuracy of the proposed CKF method is evaluated in indoor field tests by a self-developed Bluetooth fingerprint positioning system. The conducted field tests demonstrate the effectiveness of the method in providing an accurate indoor positioning solution.},
  keywords = {Bayes methods;Bluetooth;indoor communication;Kalman filters;RSSI;sequential estimation;indoor localization;received signal strength indicators;RSSI;state model;constrained Kalman filter;CKF;Bayesian framework;motion model;statistical property;positioning accuracy;indoor field tests;Bluetooth fingerprint positioning system;indoor positioning solution;Kalman filters;Position measurement;Bluetooth;Estimation;Bayes methods;Phase measurement;Kalman filter;fingerprinting;receiver signal strength indicator (RSSI);Bayesian estimation},
  doi = {10.1109/EUSIPCO.2015.7362717},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570104665.pdf},
}

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Due to the lack of an analytic formula for the fingerprinting measurements, the Kalman filter can not be directly applied. By introducing a hidden variable to represent the unknown positioning coordinate, a state model is formulated and a constrained Kalman filter (CKF) is then derived within the Bayesian framework. The update of the state incorporates the prior information of the motion model and the statistical property of the hidden variable estimated from the RSSI measurements. The positioning accuracy of the proposed CKF method is evaluated in indoor field tests by a self-developed Bluetooth fingerprint positioning system. The conducted field tests demonstrate the effectiveness of the method in providing an accurate indoor positioning solution.","keywords":"Bayes methods;Bluetooth;indoor communication;Kalman filters;RSSI;sequential estimation;indoor localization;received signal strength indicators;RSSI;state model;constrained Kalman filter;CKF;Bayesian framework;motion model;statistical property;positioning accuracy;indoor field tests;Bluetooth fingerprint positioning system;indoor positioning solution;Kalman filters;Position measurement;Bluetooth;Estimation;Bayes methods;Phase measurement;Kalman filter;fingerprinting;receiver signal strength indicator (RSSI);Bayesian estimation","doi":"10.1109/EUSIPCO.2015.7362717","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570104665.pdf","bibtex":"@InProceedings{7362717,\n author = {L. Chen and H. Kuusniemi and Y. Chen and J. Liu and L. Pei and L. Ruotsalainen and R. Chen},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {Constraint Kalman filter for indoor bluetooth localization},\n year = {2015},\n pages = {1915-1919},\n abstract = {This paper studies sequential estimation of indoor localization based on fingerprints of received signal strength indicators (RSSI). Due to the lack of an analytic formula for the fingerprinting measurements, the Kalman filter can not be directly applied. By introducing a hidden variable to represent the unknown positioning coordinate, a state model is formulated and a constrained Kalman filter (CKF) is then derived within the Bayesian framework. The update of the state incorporates the prior information of the motion model and the statistical property of the hidden variable estimated from the RSSI measurements. The positioning accuracy of the proposed CKF method is evaluated in indoor field tests by a self-developed Bluetooth fingerprint positioning system. The conducted field tests demonstrate the effectiveness of the method in providing an accurate indoor positioning solution.},\n keywords = {Bayes methods;Bluetooth;indoor communication;Kalman filters;RSSI;sequential estimation;indoor localization;received signal strength indicators;RSSI;state model;constrained Kalman filter;CKF;Bayesian framework;motion model;statistical property;positioning accuracy;indoor field tests;Bluetooth fingerprint positioning system;indoor positioning solution;Kalman filters;Position measurement;Bluetooth;Estimation;Bayes methods;Phase measurement;Kalman filter;fingerprinting;receiver signal strength indicator (RSSI);Bayesian estimation},\n doi = {10.1109/EUSIPCO.2015.7362717},\n issn = {2076-1465},\n month = {Aug},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570104665.pdf},\n}\n\n","author_short":["Chen, L.","Kuusniemi, H.","Chen, Y.","Liu, J.","Pei, L.","Ruotsalainen, L.","Chen, R."],"key":"7362717","id":"7362717","bibbaseid":"chen-kuusniemi-chen-liu-pei-ruotsalainen-chen-constraintkalmanfilterforindoorbluetoothlocalization-2015","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570104665.pdf"},"keyword":["Bayes methods;Bluetooth;indoor communication;Kalman filters;RSSI;sequential estimation;indoor localization;received signal strength indicators;RSSI;state model;constrained Kalman filter;CKF;Bayesian framework;motion model;statistical property;positioning accuracy;indoor field tests;Bluetooth fingerprint positioning system;indoor positioning solution;Kalman filters;Position measurement;Bluetooth;Estimation;Bayes methods;Phase measurement;Kalman filter;fingerprinting;receiver signal strength indicator (RSSI);Bayesian estimation"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2015url.bib","creationDate":"2021-02-13T17:31:52.510Z","downloads":0,"keywords":["bayes methods;bluetooth;indoor communication;kalman filters;rssi;sequential estimation;indoor localization;received signal strength indicators;rssi;state model;constrained kalman filter;ckf;bayesian framework;motion model;statistical property;positioning accuracy;indoor field tests;bluetooth fingerprint positioning system;indoor positioning solution;kalman filters;position measurement;bluetooth;estimation;bayes methods;phase measurement;kalman filter;fingerprinting;receiver signal strength indicator (rssi);bayesian estimation"],"search_terms":["constraint","kalman","filter","indoor","bluetooth","localization","chen","kuusniemi","chen","liu","pei","ruotsalainen","chen"],"title":"Constraint Kalman filter for indoor bluetooth localization","year":2015,"dataSources":["eov4vbT6mnAiTpKji","knrZsDjSNHWtA9WNT"]}