Recursive Bayesian Filters for RSS-Based Device-Free Localization and Tracking

Kaltiokallio, Ossi; Hostettler, Roland; Patwari, Neal; Jäntti, Riku

doi:10.1109/ipin.2018.8533772

Cited by 8 publications

(15 citation statements)

References 19 publications

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“…the heading and its location. Kaltiokallio et al [72] compared the relative performance of PF and EKF. The study concluded that for indoor positioning based on RSS, they are largely similar with the exception of the computational overhead, which favours the EKF.…”

Section: Methods Of Fusionmentioning

confidence: 99%

Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

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The importance of accurate and efficient positioning and tracking is widely understood. However, there is a pressing lack of progress in the standardisation of methods, as well as generalised framework of their evaluation. The aim of this survey is to discuss the currently prevalent and emerging types of sensors used for location estimation. The intent of this review is to take account of this taxonomy and to provide a wider understanding of the current state-of-the-art. To that end, we outline various sensor modalities, as well as popular fusion and integration techniques, discussing how their combinations can help in various application settings. Firstly, we present the fundamental mechanics behind sensors employed by the localisation community. Furthermore we outline the formal theory behind prominent fusion methods and provide exhaustive implementation examples of each. Finally, we provide points for future discussion regarding localisation sensing, fusion and integration methods.to user's preference and experience. This makes the relative selection space large, and frequently open to interpretation with regard to available resources and constraints.Whilst the survey literature pertaining to localisation systems and methods is large [54,171,181], there exists very little in the way of localisation-centric sensor utilisation. This encompasses the use of bespoke [39] or off-the-shelf [13,118] sensors, specifically for the use of location estimation, robustification and optimisation. This area is extensive [20,35,57,83,136,146], yet very often bundled along with localisation technology surveys, without subsequent scrutiny. We aim to close this gap, by reviewing sensors, their fusion and utilisation as applied to localisation, in contrast to localisation methods, technologies and implementations themselves.Most of the existing localisation surveys include technology-specific reviews [30,54,92,181]. They concentrate upon the methods and algorithms related to indoor localisation [30,92], techniques and technologies [181]. Some work also addresses localisation from the perspective of the device itself, such as smartphones [171]. Xiao et al. study [171] is the most closely related work to our proposed examination. The main difference is, that instead of reviewing the devices as

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Section: Methods Of Fusionmentioning

confidence: 99%

Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

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“…Some researchers used RSS-based algorithms for localization, e.g. Kaltiokallio et al used RSS in a recursive particle filter to achieve a high localization accuracy, but it also required a high number of reference tags [22]. Hsiao et al used the "Real Time Location System" with multiple reference tags to enhance the localization accuracy of a person equipped with RFID tags [23].…”

Section: Related Workmentioning

confidence: 99%

Localization of Moving Objects Based on RFID Tag Array and Laser Ranging Information

et al. 2019

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RFID (radio-frequency identification) technology is rapidly emerging for the localization of moving objects and humans. Due to the blockage of radio signals by the human body, the localization accuracy achieved with a single tag is not satisfactory. This paper proposes a method based on an RFID tag array and laser ranging information to address the localization of live moving objects such as humans or animals. We equipped a human with a tag array and calculated the phase-based radial velocity of every tag. The laser information was, first, clustered through the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm and then laser-based radial velocity was calculated. This velocity was matched with phase-based radial velocity to get best matching clusters. A particle filter was used to localize the moving human by fusing the matching results of both velocities. Experiments were conducted by using a SCITOS G5 service robot. The results verified the feasibility of our approach and proved that our approach significantly increases localization accuracy by up to 25% compared to a single tag approach.

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“…In the first, the person is located using an imaging approach [9], [10] and a Kalman filter (KF) is used for tracking [11], [12]. In the second, a propagation model together with a nonlinear Bayesian filter such as a particle filter (PF) [13], [14], [15] or an extended Kalman filter (EKF) [16] is used to track the kinematic state of the target. The considered problem can be solved more accurately using a nonlinear Bayesian filter, however, these filters are known to suffer from divergence issues if the modeling errors are significant [17, p.128].…”

Section: Introductionmentioning

confidence: 99%

“…The work is motivated by deriving the posterior Cramér-Rao bound (PCRB) for the RSS-based DFLT problem and evaluating two estimators with respect to the bound. The used RSS-based DFLT estimators are: an EKF-based method [16] and a modified radio tomographic imaging (RTI) method [12]. The analysis clearly shows that RTI is lower bounded by the pixel size of the discretized image and this bound is significantly higher than the PCRB.…”

Section: Introductionmentioning

confidence: 99%

A Novel Bayesian Filter for RSS-Based Device-Free Localization and Tracking

Kaltiokallio

Hostettler

Patwari

2021

IEEE Trans. on Mobile Comput.

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Received signal strength based device-free localization applications utilize a model that relates the measurements to position of the wireless sensors and person, and the underlying inverse problem is solved either using an imaging method or a nonlinear Bayesian filter. In this paper, it is shown that Bayesian filters nearly reach the posterior Cramér-Rao bound and they are superior with respect to imaging approaches in terms of localization accuracy because the measurements are directly related to position of the person. However, Bayesian filters are known to suffer from divergence issues and in this paper, the problem is addressed by introducing a novel Bayesian filter. The developed filter augments the measurement model of a Bayesian filter with position estimates from an imaging approach. This bounds the filter's measurement residuals by the position errors of the imaging approach and as an outcome, the developed filter has robustness of an imaging method and tracking accuracy of a Bayesian filter. The filter is demonstrated to achieve a localization error of 0.11 m in a 75 m 2 open indoor deployment and an error of 0.29 m in a 82 m 2 apartment experiment, decreasing the localization error by 30 − 48 % with respect to a state-of-the-art imaging method.

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Recursive Bayesian Filters for RSS-Based Device-Free Localization and Tracking

Cited by 8 publications

References 19 publications

Sensor Modalities and Fusion for Robust Indoor Localisation

Sensor Modalities and Fusion for Robust Indoor Localisation

Localization of Moving Objects Based on RFID Tag Array and Laser Ranging Information

A Novel Bayesian Filter for RSS-Based Device-Free Localization and Tracking

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