Bisecting k-means based fingerprint indoor localization

Chen, Yuxing; Liu, Wei; Zhao, Haojie; Cao, Shulin; Fu, Shuai; Jiang, Dingde

doi:10.1007/s11276-019-02222-0

Cited by 8 publications

(1 citation statement)

References 13 publications

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“…Meanwhile, there are also some algorithms which achieve much excellent performance in their experiments, such as the best bin fast based WKNN (BBF-WKNN) algorithm proposed in [46] achieves a ME of 1.5 m and a MaxE of over 6 m; a DNN based algorithm CellinDeep is proposed in [47] which achieves a ME of 0.78 m and a MaxE of about 5.7 m; a Bisecting K-means (BKM) algorithm proposed in [48] achieves a ME of 1.51 m and a MaxE of about 10 m. Although these algorithms achieve much excellent performance, the established radio map is too heavy to be practical. Moreover, the outliers exist in these algorithms as well.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Smartphone Indoor Positioning Scheme with Outlier Removal Using Machine Learning

et al. 2021

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In smartphone indoor positioning, owing to the strong complementarity between pedestrian dead reckoning (PDR) and WiFi, a hybrid fusion scheme of them is drawing more and more attention. However, the outlier of WiFi will easily degrade the performance of the scheme, to remove them, many researches have been proposed such as: improving the WiFi individually or enhancing the scheme. Nevertheless, due to the inherent received signal strength (RSS) variation, there still exist some unremoved outliers. To solve this problem, this paper proposes the first outlier detection and removal strategy with the aid of Machine Learning (ML), so called WiFi-AGNES (Agglomerative Nesting), based on the extracted positioning characteristics of WiFi when the pedestrian is static. Then, the paper proposes the second outlier detection and removal strategy, so called WiFi-Chain, based on the extracted positioning characteristics of WiFi, PDR, and their complementary characteristics when the pedestrian is walking. Finally, a hybrid fusion scheme is proposed, which integrates the two proposed strategies, WiFi, PDR with an inertial-navigation-system-based (INS-based) attitude heading reference system (AHRS) via Extended Kalman Filter (EKF), and an Unscented Kalman Filter (UKF). The experiment results show that the two proposed strategies are effective and robust. With WiFi-AGNES, the minimum percentage of the maximum error (MaxE) is reduced by 66.5%; with WiFi-Chain, the MaxE of WiFi is less than 4.3 m; further the proposed scheme achieves the best performance, where the root mean square error (RMSE) is 1.43 m. Moreover, since characteristics are universal, the proposed scheme integrated the two characteristic-based strategies also possesses strong robustness.

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Section: Related Workmentioning

confidence: 99%