A Kriging algorithm for location fingerprinting based on received signal strength

Liu, Chao; Kiring, Aroland; Salman, Naveed; Mihaylova, Lyudmila; Esnaola, Iñaki

doi:10.1109/sdf.2015.7347695

Cited by 24 publications

(16 citation statements)

References 10 publications

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“…The previous paragraph described the necessity for sparse channel models in model-fingerprinting-based RSS. To introduce sparseness in RSS fingerprints, Liu et al [20] employ 'ordinary' kriging (i.e. spatial interpolation) to estimate the RSS values at arbitrary locations given existing fingerprints.…”

Section: B Response Adaptive Modellingmentioning

confidence: 99%

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Response Adaptive Modelling for Reducing the Storage and Computation of RSS-Based VLP

Bastiaens

David

Martens

et al. 2018

2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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The precise (location) tracking of automated guided vehicles will be key in enlarging the productivity, efficiency and safety in the connected warehouse or production infrastructure. Combining the modest price tag, the adequate coverage and the potential centimetre accuracy makes Visible Light Positioning (VLP) systems appealing as replacements for the current, high-cost, tracking systems. Model-fingerprinting-based received signal strength (RSS) VLP enables the required accuracy. It requires an elaborate optical channel model fingerprinted in a fine-grained, and predefined positioning grid. Depending on the grid's granularity, constructing the fingerprint database demands a significant computation and storage effort. This paper employs response adaptive or sequential experimental design to form sparse channel models, vastly reducing the storage and computation. It is shown that model-fingerprinting-based RSS only requires modelling less than 1 percent of the grid points, in an elementary positioning cell. The sparse model can be re-evaluated as a way to cope with environment changeover. Model recomputation as a way of compensating for LED ageing is also studied.

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Section: B Response Adaptive Modellingmentioning

confidence: 99%

“…The SED approaches can be combined with other sparse modelling approaches i.a. Gaussian Mixture Modelling (only store the Gaussian parameters), Kriging [20] or sparse dictionaries [21].…”

Section: B Response Adaptive Modellingmentioning

confidence: 99%

Response Adaptive Modelling for Reducing the Storage and Computation of RSS-Based VLP

Bastiaens

David

Martens

et al. 2018

2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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“…There exist several proposals for routes smoothing, however in this paper, the well known Alpha-Beta filter [35], [36] is used. The reasons to employ the Alpha-Beta filter are the lower complexity, the lesser computational cost and that system model details are not required.…”

Section: Alpha-beta Filter For Noise Reductionmentioning

confidence: 99%

“…There exist a wide range of classical estimators in the literature. However, an interesting approach is given by the use an interpolation technique called Kriging [36], [37].…”

Section: Adaptive Fingerprinting Updatementioning

confidence: 99%

Adaptive Fingerprinting in Multi-Sensor Fusion for Accurate Indoor Tracking

et al. 2017

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Abstract-Indoor Localization and Tracking have become an attractive research topic because of the wide range of potential applications. These applications are highly demanding in terms of estimation accuracy and rise a challenge due to the complexity of the scenarios modeled. Approaches for these topics are mainly based on either deterministic or probabilistic methods such as Kalman or Particles Filter. These techniques are improved by fusing information from different sources such as wireless or optical sensors. In this paper, a novel MUlti-sensor Fusion using Adaptive Fingerprint (MUFAF) Algorithm is presented and compared with several multi-sensor indoor localization and tracking methods. MUFAF is mainly divided in four phases: first, a Target Position Estimation (TPE) process is performed by every sensor; second, a Target Tracking Process (TTP) stage; third, a Multi-Sensor fusion (MMF) combines the sensor information and finally, an Adaptive Fingerprint Update (AFU) is applied. For TPE, a complete environment characterization in combination with a Kernel Density Estimation (KDE) technique are employed to obtain object position. A Modified Kalman Filter (MKF) is applied to TPE output in order to smooth target routes and avoid outliers effect. Moreover, two fusion methods are described in this work: Track-To-Track Fusion (TTTF) and Kalman Sensor Group Fusion (KSGF). Finally, AFU will endow the algorithm with responsiveness to environment changes by using Kriging interpolation to update the scenario fingerprint. MUFAF is implemented and compared in a testbed showing that it provides a significant improvement in estimation accuracy and long-term adaptivity to condition changes.

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“…In the meanwhile, Kriging methods, or sometimes called Gaussian process regression with a variogram, which are considered belonging to the family of Gaussian process, been adopted jointly with a RSS fingerprint database in several recent works. The simple Kriging method is used in [13], while the ordinary Kriging in [14] and the universal Kriging in [15]. The paper [16] proposes a new distance measure for wall attenuation in spatial correlation modeling for the Kriging method.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Processes for RSS Fingerprints Construction in Indoor Localization

Zhao

Liu

Mihaylova

et al. 2018

2018 21st International Conference on Information Fusion (FUSION)

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Location-based applications attract more and more attention in recent years. Examples of such applications include commercial advertisements, social networking software and patient monitoring. The received signal strength (RSS) based location fingerprinting is one of the most popular solutions for indoor localization. However, there is a big challenge in collecting and maintaining a relatively large RSS fingerprint database. In this work, we propose and compare two algorithms namely, the Gaussian process (GP) and Gaussian process with variogram, to estimate and construct the RSS fingerprints with incomplete data. The fingerprint of unknown reference points is estimated based on measurements at a limited number of surrounding locations. To validate the effectiveness of both algorithms, experiments using Bluetooth-low-energy (BLE) infrastructure have been conducted. The constructed RSS fingerprints are compared to the true measurements, and the result is analyzed. Finally, using the constructed fingerprints, the localization performance of a probabilistic fingerprinting method is evaluated.

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A Kriging algorithm for location fingerprinting based on received signal strength

Cited by 24 publications

References 10 publications

Response Adaptive Modelling for Reducing the Storage and Computation of RSS-Based VLP

Response Adaptive Modelling for Reducing the Storage and Computation of RSS-Based VLP

Adaptive Fingerprinting in Multi-Sensor Fusion for Accurate Indoor Tracking

Gaussian Processes for RSS Fingerprints Construction in Indoor Localization

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