A Low-Cost Single-Anchor Solution for Indoor Positioning Using BLE and Inertial Sensor Data

Ye, Feng; Chen, Ruizhi; Guo, Guangyi; Peng, Xuesheng; Liu, Zuoya; Huang, Lixiong

doi:10.1109/access.2019.2951281

Cited by 34 publications

(19 citation statements)

References 38 publications

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“…Inertial measurement data can also be a support for direction of arrival (DoA) and ranging based on typical 2.4 GHz protocols. For example, in [ 35 ], the authors present a fusion algorithm for BLE transceiver signal angle and distance estimation based on Kalman filtering and simplified pedestrian dead reckoning (PDR). Angle estimation is achieved with signal-phase measurements on a uniform linear antenna array, while the range is estimated with a path loss model based on RSS.…”

Section: Related Workmentioning

confidence: 99%

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%

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

Groth

Nyka

Kulas

2021

Sensors

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“…It is also adaptive and changes dynamically as the frequency channel and bit position vary. The adaptive number of reference clock cycles, N C (n, k) are given in (7) as follows:…”

Section: Proposed Adaptive All-digital Fll Designmentioning

confidence: 99%

An Ultra-Low Power, Adaptive All-Digital Frequency-Locked Loop With Gain Estimation and Constant Current DCO

et al. 2020

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In this paper, an ultra-low power, adaptive all-digital integer frequency-locked loop (FLL) with gain estimation and constant current digitally controlled oscillator (DCO) for Bluetooth low energy (BLE) transceiver in Internet-of-Things (IoT) is presented. For locking DCO frequency closest to the target channel, it adaptively controls capacitor banks with binary algorithm. With decrease in frequency resolution, DCO clock counts for each capacitor bank bit evaluation dynamically increases with the proposed technique for accurate frequency tracking. For compensating PVT variations and finding the BLE frequency deviation, the configurable digital DCO gain estimation is incorporated. The low power and constant current DCO operates in sub-threshold region and its power consumption is minimized by g m /I D methodology optimization, constant current source for limiting current in DCO core through adaptive low-dropout regulator (LDO) and lowering the supply voltage. The proposed design is integrated in an ADPLL for BLE transceiver and it is fabricated with 1P6M TSMC 55 nm CMOS technology. The all-digital adaptive FLL is fully synthesizable and its area is 1800 µm 2 with 1.233 K gate count. The RMS current consumption is 103.32 µA from 1 V voltage supply with 103.32 µW power requirement. The experimental results reveal, DCO draws 480 µA current from 0.55 V supply voltage at center frequency. It has frequency resolution of 4.8 kHz. The oscillator PN, FOM and FOM T at 1-MHz offset frequency from 2.44 GHz carrier frequency are −122.85 dBc/Hz, 196.38 dBc/Hz and 208.19 dBc/Hz, respectively. INDEX TERMS Adaptive controller, all-digital frequency locked loop, digitally controlled oscillator, gain estimation, constant current, LDO, ultra-low power, synthesizable.

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“…Radio-based indoor positioning can generally be divided into three groups: Proximity, Time of Flight (TOF) measurements, and Received Signal Strength Indicator (RSSI)-based methods [ 15 ]. In some cases, these have also been supplemented with an Angle of Arrival (AOA) localization approach [ 16 ]. Depending on the localization approach chosen to calculate the current position, the following methods are most commonly used: Triangulation, trilateration and fingerprinting.…”

Section: Introductionmentioning

confidence: 99%

“…For indoor localization purposes, the Inertial Navigation System (INS) [ 16 , 31 ] based on the Inertial Measurement Unit (IMU) is often used as a complementary system to radio- or visual-based localization. The pedestrian inertial navigation system, which uses a dead reckoning approach, usually consists of a pedometer (or step counter) and a digital compass, which enable to calculate the current pose according to the starting point [ 3 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

An Automated Indoor Localization System for Online Bluetooth Signal Strength Modeling Using Visual-Inertial SLAM

Tomažič

Škrjanc

2021

Sensors

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Indoor localization is becoming increasingly important but is not yet widespread because installing the necessary infrastructure is often time-consuming and labor-intensive, which drives up the price. This paper presents an automated indoor localization system that combines all the necessary components to realize low-cost Bluetooth localization with the least data acquisition and network configuration overhead. The proposed system incorporates a sophisticated visual-inertial localization algorithm for a fully automated collection of Bluetooth signal strength data. A suitable collection of measurements can be quickly and easily performed, clearly defining which part of the space is not yet well covered by measurements. The obtained measurements, which can also be collected via the crowdsourcing approach, are used within a constrained nonlinear optimization algorithm. The latter is implemented on a smartphone and allows the online determination of the beacons’ locations and the construction of path loss models, which are validated in real-time using the particle swarm localization algorithm. The proposed system represents an advanced innovation as the application user can quickly find out when there are enough data collected for the expected radiolocation accuracy. In this way, radiolocation becomes much less time-consuming and labor-intensive as the configuration time is reduced by more than half. The experiment results show that the proposed system achieves a good trade-off in terms of network setup complexity and localization accuracy. The developed system for automated data acquisition and online modeling on a smartphone has proved to be very useful, as it can significantly simplify and speed up the installation of the Bluetooth network, especially in wide-area facilities.

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A Low-Cost Single-Anchor Solution for Indoor Positioning Using BLE and Inertial Sensor Data

Cited by 34 publications

References 38 publications

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

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

An Ultra-Low Power, Adaptive All-Digital Frequency-Locked Loop With Gain Estimation and Constant Current DCO

An Automated Indoor Localization System for Online Bluetooth Signal Strength Modeling Using Visual-Inertial SLAM

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