The effects of dynamic environment on channel frequency response-based indoor positioning

Magsino, Elmer R.; Ho, Ivan Wang‐Hei; Situ, Zhenhui

doi:10.1109/pimrc.2017.8292442

Cited by 17 publications

(13 citation statements)

References 12 publications

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Section: Trrs-based Positioningmentioning

confidence: 99%

“…The training database can be updated at regular intervals such that each known location can now be represented as a historical data of fingerprints. Thus, we adopted the following changes in the TRRS algorithm to allow multiple training fingerprints per location [34]: Continuous fingerprint appending (CFA): We find the best location estimate given training fingerprints collected across several positions and time stamps. Fingerprint averaging (FA): Each location is represented by the average of all the fingerprints collected at different times from the same location. We find the estimate by comparing the testing fingerprint against the averaged fingerprints. Weighted fingerprint averaging (WFA): Similar to FA except that more weight is given to more recent fingerprints. …”

Section: Experiments On Csi Fingerprinting‐based Ipsmentioning

confidence: 99%

“…Fingerprints from the same position may have less TRRS similarity if there are changes in the environment. However, TRRS w/ CFA requires more storage space [34] and slightly longer processing time [28] than the other two TRRS remedies.…”

Section: Experiments On Csi Fingerprinting‐based Ipsmentioning

confidence: 99%

“…Correlation between CFR fingerprints using TRRS can decay from 90 to 70% over four days [20]. When the correlation is severely degraded, and the channel stationarity cannot be met, IPS require database maintenance as often as once a day [34]. IPS should, therefore, be more robust against these changes to avoid frequent update and maintenance.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

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Survey of CSI fingerprinting‐based indoor positioning and mobility tracking systems

Rocamora

Mak

et al. 2020

IET signal process.

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Techniques for indoor positioning systems (IPSs) can be categorised as range-based or range-free. Range-based methods rely on geometric mappings to approximate a location given the calculated distances or angles from multiple reference points. In contrast, range-free strategies utilise fingerprinting, wherein an acquired fingerprint data is compared to a precollected dataset to identify the best position estimate. Among these, fingerprinting of channel state information (CSI) is preferred over other information such as received signal strength indicator as the former can exploit the effect of multipath propagation and is robust against non-line-of-sight channels. CSI has the potential to achieve cm-level positioning accuracy with a single reference point only. In this study, the authors survey CSI fingerprinting-based indoor positioning and mobility tracking systems. The process of fingerprinting that includes site surveying and signal preprocessing is discussed in detail. They determine the potential challenges of such systems and propose remedies to improve positioning accuracy. In general, spatial diversity, such as multiple-input multiple-output antennas and wireless sensor networks, or frequency diversity (e.g. high subcarrier count, frequency hopping mechanism) are exploited to achieve high positioning resolution. Such IPS can also be enhanced via additional sensors or spatial graphs for motion detection and tracking.

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Section: Trrs-based Positioningmentioning

confidence: 99%

Section: Experiments On Csi Fingerprinting‐based Ipsmentioning

confidence: 99%

Section: Experiments On Csi Fingerprinting‐based Ipsmentioning

confidence: 99%

Section: Challenges and Future Directionsmentioning

confidence: 99%

See 2 more Smart Citations

Survey of CSI fingerprinting‐based indoor positioning and mobility tracking systems

Rocamora

Mak

et al. 2020

IET signal process.

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“…Another major shortcoming of this technique is that it suffers from performance deterioration over time and requires periodical re‐training [6]. This can happen due to changes in the layout of the space [7], or through deliberate hostile action [8]. It is, therefore, in the best interest of the system for the fingerprinting method to be as simple and efficient as possible, in order to be easily performed when required.…”

Section: Introductionmentioning

confidence: 99%

H4LO: automation platform for efficient RF fingerprinting using SLAM‐derived map and poses

Kozłowski

Twomey

Byrne

et al. 2020

IET Radar, Sonar & Navigation

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One of the main shortcomings of Received Signal Strength based indoor localisation techniques is the labour and time cost involved in acquiring labelled 'ground-truth' training data. This training data is often obtained through fingerprinting, which involves the user visiting all prescribed locations to capture sensor observations throughout the environment. These prescribed sites must be annotated with reference coordinates which correspond to a known floor plan. In this work, we present 'H4LO' (Helmet for Localisation Optimisation): a low-cost robotic system designed to cut down on the labour by utilising an off-the-shelf Light Detection and Ranging device. This system allows for Simultaneous Localisation and Mapping, providing the human user with an accurate pose estimation and a corresponding map of the environment. The high resolution location estimation can then be used to train a positioning model, where Received Signal Strength data is acquired from a human-worn wearable device. The method is evaluated using live measurements, recorded within a residential property in Bristol. We compare the ground-truth location labels generated automatically by the H4LO system with a camerabased fingerprinting technique from previous work. We find that the system remains comparable in performance to the less-efficient camera-based method, whilst removing the need for time-consuming labour associated with registering the user's location. 1

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Indoor Localization of a Multi-story Residential Household using Multiple WiFi Signals

Magsino

Sim

Tagabuhin

et al. 2021

2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

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The effects of dynamic environment on channel frequency response-based indoor positioning

Cited by 17 publications

References 12 publications

Survey of CSI fingerprinting‐based indoor positioning and mobility tracking systems

Survey of CSI fingerprinting‐based indoor positioning and mobility tracking systems

H4LO: automation platform for efficient RF fingerprinting using SLAM‐derived map and poses

Indoor Localization of a Multi-story Residential Household using Multiple WiFi Signals

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