A Particle Filter Based Reference Fingerprinting Map Recalibration Method

Chu, Cong Qiu; Yang, Shidong

doi:10.1109/access.2019.2931992

Cited by 12 publications

(8 citation statements)

References 32 publications

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“…In [ 23 ], invariant RSS statistics were introduced to eliminate the need for offline recalibration. Particle filters can also be adopted to use crowd-sourced fingerprinting maps for the recalibration [ 21 ] and to fuse PDR and positioning estimation data in order to determine and re-estimate the divergence of particle trajectories. These re-estimated trajectories can be adopted to update the radio map.…”

Section: Related Workmentioning

confidence: 99%

“…In dynamic environments, the system needs to be recalibrated regularly as the radio map becomes outdated when the propagation environment changes over time. A number of solutions have been researched to simplify the recalibration process [ 21 , 22 , 23 ]; nevertheless, in most cases, additional infrastructure is needed. The other drawback is the number of reference nodes that need to be installed on site to collect information about the signal.…”

Section: Introductionmentioning

confidence: 99%

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Calibration-Free Single-Anchor Indoor Localization Using an ESPAR Antenna

Groth

Nyka

Kulas

2021

Sensors

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In this paper, we present a novel, low-cost approach to indoor localization that is capable of performing localization processes in real indoor environments and does not require calibration or recalibration procedures. To this end, we propose a single-anchor architecture and design based on an electronically steerable parasitic array radiator (ESPAR) antenna and Nordic Semiconductor nRF52840 utilizing Bluetooth Low Energy (BLE) protocol. The proposed algorithm relies on received signal strength (RSS) values measured by the receiver equipped with the ESPAR antenna for every considered antenna radiation pattern. The calibration-free concept is achieved by using inexpensive BLE nodes installed in known positions on the walls of the test room and acting as reference nodes for the positioning algorithm. Measurements performed in the indoor environment show that the proposed approach can successfully provide positioning results better than those previously reported for single-anchor ESPAR antenna localization systems employing the classical fingerprinting method and relying on time-consuming calibration procedures.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Calibration-Free Single-Anchor Indoor Localization Using an ESPAR Antenna

Groth

Nyka

Kulas

2021

Sensors

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“…The resulting set of low-grade particles is a poor representation of the probability distribution. To increase the variety of resampling particles, resampling has been extensively studied, and many resampling schemes have been proposed (e.g., [18], [19], [37], [38]). In [18], the authors offered a sophisticated algorithm to check the particle quality after resampling based on the threshold value.…”

Section: B Particle Degradation Problemmentioning

confidence: 99%

“…Conversely, a PF is effective for non-Gaussian and nonlinear environments [17]. Moreover, a PF is more robust than a linear filter and more flexible in representing the posterior [18]. By contrast, a traditional PF may cause a particle degradation during the resampling phase [19].…”

Section: Introductionmentioning

confidence: 99%

Floor Map-Aware Particle Filtering Based Indoor Navigation System

Junoh

Subedi²,

Pyun

2021

IEEE Access

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Smartphone-based indoor navigation systems are becoming increasingly popular in a variety of applications. However, localization accuracy has always been a challenge. The Kalman filter (KF) is a well-known estimation in the Bayesian framework, but can only deal with linear problems and Gaussian models. A particle filter (PF) is another essential estimation tool in a Bayesian system. However, a critical challenge with PF is the problem of particle degradation after resampling. To mitigate the particle degradation problem in PF, unsupervised learning based on k-means clustering is proposed in this paper. It forms clusters of similar particles based on the sum of weights. Also, we present enhancing the PF by utilizing a map constraint and k-means clustering (PFMK) and integrating Bluetooth low energy (BLE) along with pedestrian dead reckoning (PDR) for positioning. BLE and PDR-based positioning with a map constraint lead to an increase in accuracy of at least 20% compared with a traditional PF. Moreover, the proposed unsupervised k-means approach increases the accuracy by an additional 20%, whereas the overall performance of PFMK achieves a mean error of <1.5 m in the test environments.INDEX TERMS Bluetooth low energy (BLE) beacons, indoor navigation, map constraints, particle filter, unsupervised learning.

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“…Different conditions and implementations have been considered, such as NLOS in narrow-band systems [36], positioning optimization in wireless sensor networks [37] or enhancement of positions and orientation estimations [38] . The analysis on positioning bounds [39], the use of techniques such as map re-calibration [40] and [41] or multiple data fusion [42] provide further enhancement in PF -based location. These results have also been extended to the case of UWB systems, proposing Generalized Gaussian Mixture filters [43] or the use of round trip time [45] information to increase location accuracy.…”

Section: Positioning Using a Particle Filtermentioning

confidence: 99%

Towards Sub-Meter Level UWB Indoor Localization Using Body Wearable Sensors

et al. 2020

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Thanks to its ability to provide sub-meter level positioning accuracy, Ultrawideband (UWB) has found wide use in several wireless body area network (WBAN) applications such as ambient assisted living, remote patient management and preventive care, among others. In spite of the attractiveness of UWB, it is not possible to achieve this level of accuracy when the human body obstructs the wireless channel, leading to a bias in the Time of Flight (TOF) measurements, and hence a detection of position errors of several meters. In this paper, a study of how a sub-meter level of accuracy can be achieved after compensating for body shadowing is presented. Using a Particle Filter (PF), we apply UWB ranging error models that take into consideration the body shadowing effect and evaluate them through simulations and extensive measurements. The results show a significant reduction in the median position error of up to 75 % and 82 % for simulations and experiments, respectively, leading to the achievement of a sub-meter level of localization accuracy. INDEX TERMS Body wearable sensors, Human body shadowing, Localization, Ranging, Ultrawideband, Time of Flight, Particle Filter I. INTRODUCTION With rapid developments of computer and miniaturization technologies, wearable sensors are becoming an important part of our daily lives. With a market expected to grow to $ 70 billion in 2025 [1], wearables are widely used in several applications such as in sport science, rehabilitation, medical monitoring, surveillance, among others [2]-[4]. Therefore, they can be attached to shoes, eyeglasses, earrings, clothing, gloves and watches, etc of the user of the wearable. Collecting precise user localization information is one of the salient capabilities of wearables [5]. In fact, according to [6], a sub-meter level of localization accuracy is envisaged for several wireless body area networks (WBAN). Currently, Global Navigation Satellite System (GNSS) is the most widely spread localization technology in outdoor environments.

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A Particle Filter Based Reference Fingerprinting Map Recalibration Method

Cited by 12 publications

References 32 publications

Calibration-Free Single-Anchor Indoor Localization Using an ESPAR Antenna

Calibration-Free Single-Anchor Indoor Localization Using an ESPAR Antenna

Floor Map-Aware Particle Filtering Based Indoor Navigation System

Towards Sub-Meter Level UWB Indoor Localization Using Body Wearable Sensors

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