Wireless indoor positioning system with inertial sensors and infrared beacons

Januszkiewicz, Łukasz; Kawecki, Jarosław; Kawecki, Robert

doi:10.1109/eucap.2016.7481650

Cited by 11 publications

(4 citation statements)

References 4 publications

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“…The direction of movement is calculated using the data from sensors placed on the shirt and in the shoe. This solution was proposed to improve the accuracy of the angle detection compared to the older version of the system that was using only sensors attached to shirt [11]. In addition, placing inertial sensors in the shoe allows to estimate the length of the step more accurately which was not possible in a system with only sensors placed in a T-shirt.…”

Section: Methods -Localization Algorithmmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Kawecki

Januszkiewicz

2019

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This article presents a wearable system that localizes people in the indoor environment, using data from inertial sensors. The sensors measure the parameters of human motion, tracking the movements of the torso and foot. For this purpose, they were integrated with shirt and the shoe insole. The values of acceleration measured by the sensors are sent via Bluetooth to a smartphone. The localization algorithm implemented on the smartphone, presented here, merges data from the shirt and the shoe to track the steps made by the user and filter out the localization errors caused by movements the shirt and torso. The experimental verification of the algorithm is also presented.

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Section: Methods -Localization Algorithmmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Kawecki

Januszkiewicz

2019

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“…From a different perspective, Januszkiewicz et al 31 tried to estimate movements and track user's linear and axial acceleration by strapping an accelerometer and gyroscope sensor to the users arm. The system was evaluated in a room of 26 × 12 m, where the participant was required to walk the predefined route so that the estimated position and real position can be compared.…”

Section: Location Estimation and Navigationmentioning

confidence: 99%

Multienvironment performance comparison of robot‐assisted indoor location estimation system

Perente

Şerif

2020

Concurrency and Computation

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Many indoor location systems utilizing the fingerprinting technique require frequent signal map generation due to the dynamic properties of indoor environments. This time-consuming task can be freed from human involvement by using various techniques. Thus, a robot-aided signal map creator is implemented and its positioning performance as well as its mapping time is compared with a widely used human facilitated approach. For testing purposes, a low-cost, low-power, not off-the-shelf signal data collection robot is implemented. Its signal collection speed and location estimation precision is then compared with the manual method both in a small and large environment.The findings indicate that in the small environment, robot-aided approach performs with 268-cm error-rate 70% of the time. On the other hand, the improved robot-aided approach in large environment saves 52 min of labor-intense legwork and achieves 303.5-cm mean error-rate.

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“…Wireless local area networks (WLANs), INSs, and Bluetooth low-energy (BLE) wireless beacons can be combined to achieve calibration-free hybrid inside positioning [16]. BLE can successfully improve the accuracy of INS and be adopted to determine the initial position of the users [17]. A map-matching method can be used to correct the DR system to realize positioning continuously [18].…”

Section: Introductionmentioning

confidence: 99%

WiFi/PDR integrated navigation with robustly constrained Kalman filter

Long

Qin

et al. 2020

Meas. Sci. Technol.

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Nowadays, global navigation satellite systems (GNSSs) are widely used in location-based services (LBSs) as they can provide high-accuracy position services continuously. However, their performance deteriorates in indoor scenarios, in which GNSS signal reception is limited or completely impossible. In this paper, an enhanced constrained Kalman filter is presented to enhance the indoor positioning performance of a LBS and for use in a WiFi/pedestrian dead reckoning (PDR) integrated navigation algorithm. A robust scheme for computing the gross error in constrained conditions is suggested to make the performance of the constrained condition model in the WiFi/PDR integrated system more robust. The results of simulation analysis indicate that the robustly constrained Kalman filter can reliably determine gross errors in constrained conditions. An indoor field experiment was conducted to test the performance of the proposed filter algorithm, and the results show that the improved filter can eliminate the effect of gross error from constrained conditions in the WiFi/PDR system.

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Wireless indoor positioning system with inertial sensors and infrared beacons

Cited by 11 publications

References 4 publications

International Journal of Electronics and Telecommunications

International Journal of Electronics and Telecommunications

Multienvironment performance comparison of robot‐assisted indoor location estimation system

WiFi/PDR integrated navigation with robustly constrained Kalman filter

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