The Geo-n localization algorithm

Will, Heiko; Hillebrandt, Thomas; Kyas, Marcel

doi:10.1109/ipin.2012.6418867

Cited by 18 publications

(5 citation statements)

References 19 publications

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“…In the experiments, the log distance path loss model is used for distance estimation, weighed multilateration [8], [15] and GeoN [16] as localization algorithms. These are described in the following subsections.…”

Section: B Localization Proceduresmentioning

confidence: 99%

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Channel Diversity for Indoor Localization Using Bluetooth Low Energy and Extended Advertisements

Nikodem¹,

Szelinski

2021

IEEE Access

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Bluetooth Low Energy (BLE) is a ubiquitous low-power communication technology used in many applications including location-based services. Typically, in BLE localization, the beacons transmit advertisement messages while moving devices infer their location from radio signal strength measurements. Previous work has noted that three radio channels used for advertisement transmission exhibit different propagation conditions including frequency dependent shadowing and multipath. Because most of the consumer electronic devices report only the measured signal strength value without the number of a channel in which it was measured, the variance of the measurements increases and adversely affects the accuracy of localization. Information on the channel used can improve the localization accuracy, however, the existing approaches are limited to three advertisement channels, ignoring the remaining 37 channels available in BLE communication. This article analyses the impact of channel diversity on the accuracy of BLE-based indoor localization. In contrast to previous work, we use signal strength measurements from all 40 channels and show that channel diversity can significantly improve the localization accuracy. Experiments conducted in 100 m 2 office area show that using signal strength measurements in 40 channels improves the average localization accuracy by approximately 50 % and 20 % compared to the use of 3 channels without and with information on the channel used, respectively. Overhead of the proposed method can be reduced through careful selection of the radio channels used in measurements. We propose a channel selection method which allows to significantly improve the localization accuracy using measurements collected from between 10 and 15 channels.

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Section: B Localization Proceduresmentioning

confidence: 99%

“…The second localization algorithm is Geo-N [16]. This is a geometrical algorithm that attempts to eliminate distance estimates that contribute significantly to the localization error.…”

Section: E Localization Algorithmsmentioning

confidence: 99%

Channel Diversity for Indoor Localization Using Bluetooth Low Energy and Extended Advertisements

Nikodem¹,

Szelinski

2021

IEEE Access

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“…The filtering and smoothing methods are needed to alleviate the impact of non-line-of-sight and multipath effects on the measurements and minimize these effects. Therefore, all the smoothing algorithms are implemented in an indoor tracking test-bed as introduced in our previous work [60], which consists of a robot and wireless sensor networks of CSS-TOF measuring. The experiment is carried out in a typical indoor scenario, the halls and classrooms on the first floor in our Computer Science building.…”

Section: Experiments Descriptionmentioning

confidence: 99%

Efficient Marginalized Particle Smoother for Indoor CSS–TOF Localization with Non-Gaussian Errors

et al. 2020

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The time-series state and parameter estimations of indoor localization continue to be a topic of growing importance. To deal with the nonlinear and positive skewed non-Gaussian dynamic of indoor CSS–TOF (Chirp-Spread-Spectrum Time-of-Flight) ranging measurements and position estimations, Monte Carlo Bayesian smoothers are promising as involving the past, present, and future observations. However, the main problems are how to derive trackable smoothing recursions and to avoid the degeneracy of particle-based smoothed distributions. To incorporate the backward smoothing density propagation with the forward probability recursion efficiently, we propose a lightweight Marginalized Particle Smoother (MPS) for nonlinear and non-Gaussian errors mitigation. The performance of the position prediction, filtering, and smoothing are investigated in real-world experiments carried out with vehicle on-board sensors. Results demonstrate the proposed smoother enables a great tool by reducing temporal and spatial errors of mobile trajectories, with the cost of a few sequence delay and a small number of particles. Therefore, MPS outperforms the filtering and smoothing methods under weak assumptions, low computation, and memory requirements. In the view that the sampled trajectories stay numerically stable, the MPS form is validated to be applicable for time-series position tracking.

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“…Since the performance of the MC algorithms is sensitive to the approximate position, we use the ML solution as its approximate position, which is denoted as an 'ML+MC' algorithm. The description of the LMS algorithm and the Geo-n algorithm can be found in reference [15] and reference [23], respectively.…”

Section: Positioning Precision Evaluation With the MC Algorithmmentioning

confidence: 99%

“…The intersection based non-exact solution is also claimed to be robust to NLOS measurements [18], which include the min-max approach [19], the iterative clustering-based localization (ICLA) [20], the bi-lateration approach [21], the adaptive multi-lateration (AML) [22] algorithm and the Geo-n algorithm [23]. The Geo-n algorithm is a representative algorithm of the intersection based approaches and reported outperforming other intersection based algorithms [23]. However, the intersection based algorithms are also non-exact solutions, so their location estimates may not be very accurate.…”

Section: Introductionmentioning

confidence: 99%

NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm

et al. 2019

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The presence of None-line-of-sight (NLOS) is one of the major challenging issues in time of arrival (TOA) based source localization, especially for the sparse anchor scenarios. Sparse anchors can reduce the system deployment cost, so this has become increasingly popular in the source location. However, fewer anchors bring new challenges to ensure localization precision and reliability, especially in NLOS environments. The maximum likelihood (ML) estimation is the most popular location estimator for its simplicity and efficiency, while it becomes extremely difficult to reliably identify the NLOS measurements when the redundant observations are not enough. In this study, we proposed an NLOS detection algorithm called misclosure check (MC) to overcome this issue, which intends to provide a more reliable location in the sparse anchor environment. The MC algorithm checks the misclosure of different triangles and then obtains the possible NLOS from these misclosures. By forming multiple misclosure conditions, the MC algorithm can identify NLOS measurements reliably, even in a sparse anchor environment. The performance of the MC algorithm is evaluated in a typical sparse anchor environment and the results indicate that the MC algorithm achieves promising NLOS identification capacity without abundant redundant measurements. The real data test also confirmed that the MC algorithm achieves better position precision than other three robust location estimators in an NLOS environment since it can correctly identify more NLOS measurements.

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The Geo-n localization algorithm

Cited by 18 publications

References 19 publications

Channel Diversity for Indoor Localization Using Bluetooth Low Energy and Extended Advertisements

Channel Diversity for Indoor Localization Using Bluetooth Low Energy and Extended Advertisements

Efficient Marginalized Particle Smoother for Indoor CSS–TOF Localization with Non-Gaussian Errors

NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm

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