This paper proposes a localization scheme exploiting reference signal received power (RSRP) for estimation of the next location. The proposed scheme can correct outliers without discarding data by adding RSRP as a state vector for a Kalman filter, and combining the Kalman filter with fingerprint-based localization. Performance evaluation is carried out via simulations in indoor environments. Results indicate that the proposed scheme can effectively correct outliers and enhance positioning accuracy. The root mean square error in the positioning error was reduced by 56%, compared to the conventional fingerprint-based localization schemes for indoor environments.
|Number of pages||6|
|Journal||IEIE Transactions on Smart Processing and Computing|
|Publication status||Published - 2020 Jun|
Bibliographical noteFunding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1A2C1010950).
This research was supported in part by Basic Science
This research was supported by the Tongmyong University Research Grants2019(2019F002) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education(NRF-2018R1D1A1B07048080)
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No.2018-0-00189, Security Technology for Portal Device that connects Human-Infrastructure-Service in highly trust intelligent information service)
All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering