Trilateration in Indoor Positioning with an Uncertain Reference Point

Teoman, Emre; Ovatman, Tolga

doi:10.1109/icnsc.2019.8743240

Cited by 21 publications

(21 citation statements)

References 13 publications

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“…As shown in Section 3.2, the only environment-dependent variable of the log-distance model is the path loss exponent (η). As mentioned, in most model-based IPSs [10,11,13,15] this parameter is fixed for the whole scenario. This approach is not recommended for large-scale scenarios since the path loss exponent changes from place to place depending on the obstacles and other environment variables.…”

Section: Dynamic Model Estimationmentioning

confidence: 99%

“…η mc = (η ca + η cb )/2 (10) where η ma is the path loss exponent that will be used to compute the distance between the mobile node and Anchor a , and so on. As we can see, the model parameters estimated in this part of PoDME depend mainly on the anchors that heard the packet from the mobile node.…”

Section: Dynamic Model Estimationmentioning

confidence: 99%

“…In the position computation phase, the positions of the mobile devices are computed using the distances estimations and the known position of the anchors. This computation is usually done by some optimization algorithms, such as the least-squares [10].…”

Section: Introductionmentioning

confidence: 99%

“…Most model-based IPSs proposed in the literature [10][11][12][13] use the same fixed model parameters for the whole scenario. This fixed model considers that the signals behave uniformly over the whole scenario.…”

Section: Introductionmentioning

confidence: 99%

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Indoor Positioning System Using Dynamic Model Estimation

Assayag

Oliveira

Souto

et al. 2020

Sensors

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Indoor Positioning Systems (IPSs) are used to locate mobile devices in indoor environments. Model-based IPSs have the advantage of not having an exhausting training and signal characterization of the environment, as required by the fingerprint technique. However, most model-based IPSs are done using fixed model parameters, treating the whole scenario as having a uniform signal propagation. This might work for most small scale experiments, but not for larger scenarios. In this paper, we propose PoDME (Positioning using Dynamic Model Estimation), a model-based IPS that uses dynamic parameters that are estimated based on the location the signal was sent. More specifically, we use the set of anchor nodes that received the signal sent by the mobile node and their signal strengths, to estimate the best local values for the log-distance model parameters. Also, since our solution depends highly on the selected anchor nodes to use on the position computation, we propose a novel method for choosing the three best anchor nodes. Our method is based on several data analysis executed on a large-scale, Bluetooth-based, real-world experiment and it chooses not only the nearest anchor but also the ones that benefit our least-square-based position computation. Our solution achieves a position estimation error of 3 m, which is 17% better than a fixed-parameters model from the literature.

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Section: Dynamic Model Estimationmentioning

confidence: 99%

Section: Dynamic Model Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Indoor Positioning System Using Dynamic Model Estimation

Assayag

Oliveira

Souto

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…It also gains positive effect by using unsupervised machine-learning algorithms to reduce the data dimensionality, and fingerprint matching calculations required [ 23 ]. Meanwhile, the trilateration-based positioning system is widely applied in outdoor environments [ 24 , 25 ]. However, it faces challenges in indoor environments, achieving 3–5 m accuracy without assistive technology and device, and lower stability [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning

Shi

Liu

et al. 2020

Sensors

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Received signal strength indicator (RSSI)-based positioning is suitable for large-scale applications due to its advantages of low cost and high accuracy. However, it suffers from low stability because RSSI is easily blocked and easily interfered with by objects and environmental effects. Therefore, this paper proposed a tri-partition RSSI classification and its tracing algorithm as an RSSI filter. The proposed filter shows an available feature, where small test RSSI samples gain a low deviation of less than 1 dBm from a large RSSI sample collected about 10 min, and the sub-classification RSSIs conform to normal distribution when the minimum sample count is greater than 20. The proposed filter also offers several advantages compared to the mean filter, including lower variance range with an overall range of around 1 dBm, 25.9% decreased sample variance, and 65% probability of mitigating RSSI left-skewness. We experimentally confirmed the proposed filter worked in the path-loss exponent fitting and location computing, and a 4.45-fold improvement in positioning stability based on the sample standard variance, and positioning accuracy improved by 20.5% with an overall error of less than 1.46 m.

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A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

Asaad

Maghdid

2022

Computer Networks

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Trilateration in Indoor Positioning with an Uncertain Reference Point

Cited by 21 publications

References 13 publications

Indoor Positioning System Using Dynamic Model Estimation

Indoor Positioning System Using Dynamic Model Estimation

An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning

A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

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