Deep Odometry Systems on Edge with EKF-LoRa Backend for Real-Time Indoor Positioning

Dai, Zhuangzhuang; Saputra, Muhamad Risqi U.; Lu, Chris Xiaoxuan; Tran, Vu; Wijayasingha, L. N. S.; Rahman, M Arif; Stankovic, John A.; Markham, Andrew; Trigoni, Niki

doi:10.1109/cps-er56134.2022.00007

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…In the study, factors such as the effectiveness of RSSI data processing and the stability of LoRa indoor propagation will affect the absolute positioning accuracy, so it is necessary to comprehensively analyze these factors, use appropriate hardware devices and antennas, and develop more effective RSSI data processing strategies to improve the accuracy of LoRa-based indoor positioning [15][16][17][18] . In related research fields, various methods have been proposed to solve similar problems, such as Kalman filters, moving average filters, multipath fading modelling, and calibration methods [19][20][21][22][23][24][25] .…”

Section: Related Workmentioning

confidence: 99%

Research on indoor positioning method based on LoRa-improved fingerprint localization algorithm

Chen,

Yang,

Hao

et al. 2023

Sci Rep

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Traditional fingerprint localization algorithms need help with low localization accuracy, large data volumes, and device dependence. This paper proposes a LoRa-based improved fingerprint localization algorithm-particle swarm optimization-random forest-fingerprint localization for indoor localization. The first improvement step involves creating a new exceptional fingerprint value (referred to as RSSI-RANGE) by adding the Time of Flight ranging value (referred to as RANGE) to the Received Signal Strength Indication (RSSI) value and weighting them together. The second improvement step involves preprocessing the fingerprint data to eliminate gross errors using Gaussian and median filtering. After noise reduction, the particle swarm optimization algorithm is used to optimize the hyper parameters of the random forest algorithm, and the best RSSI-RANGE value is obtained using the random forest algorithm. The Kriging method is then used for interpolation to establish an offline fingerprint database, and the final online recognition and localization are performed. Experimental results demonstrate that the first improvement step improves localization accuracy by 53–57% in different experimental scenarios, while the second improves localization accuracy by 25–31%. When both steps are combined, the localization accuracy is improved by 58–63%. The effectiveness of this method is demonstrated through experiments.

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