Robust pedestrian localization in indoor environments with an IMU aided TDoA system

Lategahn, Julian; Müller, Marcel; Röhrig, Christof

doi:10.1109/ipin.2014.7275518

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…In the server, coarse positioning is completed by the RSSI fingerprint based localization method pioneered by Jian and Hao [19]. The process of coarse positioning includes two phases [20]; the first phase is the offline stage whose main task is to establish a WiFi fingerprinting database that contains the RSSI on each reference spot collecting from the WiFi access points (APs) existing nearby; the second phase is the online phase. In this phase, coarse positioning results will be acquired through the k-Nearest Neighbor (kNN) algorithm.…”

Section: B Infrastructures Applied In the System 1) Wifi Positioning ...mentioning

confidence: 99%

Indoor Collaborative Positioning With Adaptive Particle-Pair Filtering Based on Dynamic User Pairing

Yang

Liu

et al. 2019

IEEE Access

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Technologies fusing WiFi-based indoor positioning and pedestrian dead reckoning for indoor target localization have already been proposed for a variety of occasions. Among them, the methods of multisource fusion with particle filter perform well in a random noise environment. However, the positioning accuracy can be significantly affected by the personnel density of the interest area. In this paper, we propose a novel indoor collaborative positioning system which performs dynamic user pairing and adaptive particlepair filtering with the help of the Chirp acoustic signal distance measurement. The proposed scheme mainly includes two parts in each time slot: first, the users are clustered into cells for dynamic user-pairing, and then the adaptive particle-pair filter iterates the comprehensive weight to obtain the final positioning result. The experiment results show that the proposed scheme can effectively solve the problem of the error increase caused by the intensive personnel, and remarkably improve the indoor positioning accuracy in the crowded public.INDEX TERMS Indoor positioning, dynamic user pairing, acoustic distance measurement, adaptive particle-pair filter.

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Section: B Infrastructures Applied In the System 1) Wifi Positioning ...mentioning

confidence: 99%

Indoor Collaborative Positioning With Adaptive Particle-Pair Filtering Based on Dynamic User Pairing

Yang

Liu

et al. 2019

IEEE Access

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“…Systems based on Wi‐Fi, Bluetooth, UWB, cellular networks, or RFID were proposed. Moreover, other sensors can also be used to estimate the position of mobile devices, including measurements of sound, ultrasound, magnetic field, cameras, light, or inertial forces . However, solutions based on radio signals seem to be most frequently proposed.…”

Section: Introductionmentioning

confidence: 99%

Challenges introduced by heterogeneous devices for Wi‐Fi–based indoor localization

Machaj

Brída

Majer

2019

Concurrency and Computation

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Summary In the recent time, Wi‐Fi–based indoor positioning attracts a high number of researchers worldwide because of its relatively easy implementation on widely available smart phones. Indoor positioning services can potentially open new markets for service providers and, thus, allow new ways to increase their income. A number of Wi‐Fi–enabled devices have grown significantly as well as coverage of buildings with Wi‐Fi signals, which can be considered ubiquitous nowadays. Recently, there has been a vast number of novel algorithms proposed for Wi‐Fi signals; however, they were mostly tested using only one type of device. In this paper, we will investigate the feasibility of Wi‐Fi–based positioning on different devices and highlight some of the problems that researchers have to face during the proposal of novel positioning algorithms. We have tested different devices in both laboratory and real conditions to see how they perform and which parameters can have a negative impact on the performance of the positioning system.

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“…These systems can help to provide various Location Based Services (LBS) to the users, which may produce additional income to service providers . Based on data utilized to estimate position of mobile user these systems can be divided into systems that use Inertial Measurement Unit (IMU) sensors , sound waves, light emitters, image processing and changes of magnetic field and systems that utilize signals from radio networks. All of these systems have both advantages and drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Impact of optimization algorithms on hybrid indoor positioning based on GSM and Wi‐Fi signals

Machaj

Brída

2016

Concurrency and Computation

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Summary In the recent time indoor positioning becomes an extremely hot topic between researchers worldwide. This is mainly because of lack of available positioning solutions in the indoor environment and possibility to offer novel services based on location of users. Such services can also open new markets for service providers and thus increase their income. In this paper we will focus on application of optimization algorithms on performance of hybrid indoor positioning system, which utilize radio signals from both Global System for Mobile communication and Wi‐Fi networks simultaneously. The system is based on fingerprinting framework, which is widely used for indoor positioning because of its performance in strong multipath environment. Tests of the optimization algorithms were performed via simulations that were performed using simulation model created in Matlab environment. Copyright © 2016 John Wiley & Sons, Ltd.

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Robust pedestrian localization in indoor environments with an IMU aided TDoA system

Cited by 4 publications

References 14 publications

Indoor Collaborative Positioning With Adaptive Particle-Pair Filtering Based on Dynamic User Pairing

Indoor Collaborative Positioning With Adaptive Particle-Pair Filtering Based on Dynamic User Pairing

Challenges introduced by heterogeneous devices for Wi‐Fi–based indoor localization

Impact of optimization algorithms on hybrid indoor positioning based on GSM and Wi‐Fi signals

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