IoT Edge-Computing-Enabled Efficient Localization via Robust Optimal Estimation

Qin, Shuang; Guo, Xiansheng

doi:10.1109/jiot.2022.3200095

Cited by 5 publications

(2 citation statements)

References 47 publications

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“…Various signal features were proposed and investigated to address IPP, mainly: time of arrival (TOA/TDOA) [55], angle of arrival (AOA) [56], received signal strengths (RSS) [57][58][59], and channel state information (CSI) [60,61]. In the lateration algorithm, the distance could be acquired indirectly by measuring the received signal strengths indicator (RSSI), time of arrival (TOA), or time difference of arrival (TDOA).…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, the wireless fidelity signal (Wi-Fi: 802.11) has received significant acceptance for IPS both in academia and industry communities [68] mainly for the following reasons: (a) pervasive penetration of Wireless LAN and deployment of Wi-Fi-enabled mobile devices across the globe (cost-effective as it is adopting the existing wireless network infrastructure); (b) the radio wave covers a wide range with a radius about 300 feet or the widespread of its signal over long distances; and (c) it does not require line-of-sight measurement of base stations [55,69] and achieves high applicability in a complex indoor environment. On the other hand, most indoor localization technologies based on Wi-Fi rely on received signal strengths and can be directly implemented using the existing wireless communications infrastructure without any calibrations.…”

Section: Related Workmentioning

confidence: 99%

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Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting

Gidey

Guo

Zhong

et al. 2022

Sensors

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Indoor signals are susceptible to NLOS propagation effects, multipath effects, and a dynamic environment, posing more challenges than outdoor signals despite decades of advancements in location services. In modern Wi-Fi networks that support both MIMO and OFDM techniques, Channel State Information (CSI) is now used as an enhanced wireless channel metric replacing the Wi-Fi received signal strength (RSS) fingerprinting method. The indoor multipath effects, however, make it less robust and stable. This study proposes a positive knowledge transfer-based heterogeneous data fusion method for representing the different scenarios of temporal variations in CSI-based fingerprint measurements generated in a complex indoor environment targeting indoor parking lots, while reducing the training calibration overhead. Extensive experiments were performed with real-world scenarios of the indoor parking phenomenon. Results revealed that the proposed algorithm proved to be an efficient algorithm with consistent positioning accuracy across all potential variations. In addition to improving indoor parking location accuracy, the proposed algorithm provides computationally robust and efficient location estimates in dynamic environments. A Cramer-Rao lower bound (CRLB) analysis was also used to estimate the lower bound of the parking lot location error variance under various temporal variation scenarios. Based on analytical derivations, we prove that the lower bound of the variance of the location estimator depends on the (i) angle of the base stations, (ii) number of base stations, (iii) distance between the target and the base station, djr (iv) correlation of the measurements, ρrjai and (v) signal propagation parameters σC and γ.

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