UWB Positioning System with the Support of MEMS Sensors for Indoor and Outdoor Environment

Paszek, Krzysztof; Grzechca, Damian; Tomczyk, Mateusz; Marciniak, Artur

doi:10.12720/jcm.15.6.511-518

Cited by 9 publications

(4 citation statements)

References 12 publications

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“…Then, the corrected distances are used in the trilateration process (the trilateration method based on the simplex method is used in this research). The correction mechanism is described in detail in [64].…”

Section: Pre-processing Of Datamentioning

confidence: 99%

Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects

Paszek,

Grzechca

2023

Sensors

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Automation of transportation will play a crucial role in the future when people driving vehicles will be replaced by autonomous systems. Currently, the positioning systems are not used alone but are combined in order to create cooperative positioning systems. The ultra-wideband (UWB) system is an excellent alternative to the global positioning system (GPS) in a limited area but has some drawbacks. Despite many advantages of various object positioning systems, none is free from the problem of object displacement during measurement (data acquisition), which affects positioning accuracy. In addition, temporarily missing data from the absolute positioning system can lead to dangerous situations. Moreover, data pre-processing is unavoidable and takes some time, affecting additionally the object’s displacement in relation to its previous position and its starting point of the new positioning process. So, the prediction of the position of an object is necessary to minimize the time when the position is unknown or out of date, especially when the object is moving at high speed and the position update rate is low. This article proposes using the long short-term memory (LSTM) artificial neural network to predict objects’ positions based on historical data from the UWB system and inertial navigation. The proposed solution creates a reliable positioning system that predicts 10 positions of low and high-speed moving objects with an error below 10 cm. Position prediction allows detection of possible collisions—the intersection of the trajectories of moving objects.

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Section: Pre-processing Of Datamentioning

confidence: 99%

Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects

Paszek,

Grzechca

2023

Sensors

Self Cite

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“…Although Global Positioning System (GPS) can provide satisfactory outdoor positioning service, positioning accuracy cannot be reliably guaranteed in the indoor environment [2]. At present, indoor localization technology mainly relies on RF signals [3], such as WiFi [4], Bluetooth [5], Ultra wide Band (UWB) [6], and visible light [7]. WiFi-based positioning systems are inexpensive but have poor positioning precision.…”

Section: Introductionmentioning

confidence: 99%

An indoor positioning system based on visible light and multi-sensor fusion

Tan,

Guo,

Zhao

et al. 2024

Workshop on Electronics Communication Engineering (WECE 2023)

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“…To meet the demands of indoor location services, various indoor localization technologies have been proposed, including Wi-Fi 4 , UWB 5 , Visible Light 6 , and visual methods 7 . However, currently, there lacks a unified standard that can fulfill all the requirements for indoor localization in different indoor environments.…”

Section: Introductionmentioning

confidence: 99%

Indoor localization algorithm based on geometric deep learning

Kang,

Liang,

Wang

2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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Popular machine learning based fingerprint localization methods often struggle to effectively capture non-Euclidean characteristics present in fingerprint data, while geometric deep learning can effectively process such data. In this paper, we propose a geometric fingerprinting based graph neural network indoor localization algorithm (GFGNN), which is models access points (APs) and reference points (RPs) using received signal strength (RSS) fingerprint. This approach maximizes the utilization of the unstructured nature of fingerprint data to enhance indoor localization accuracy and stability in dynamic environments. The algorithm establishes the fingerprint data as a graph feature representation, we first employ a graph convolutional network at the AP level to aggregate RSS values containing spatial relationships. Subsequently, graph isomorphism networks are employed at the RP level to further extract and update the aggregated fingerprint features. Finally, a multi-layer Perceptron is utilized to regressively predict the localization of the target to be located. We evaluate the proposed GFGNN on a self-built dataset, and the localization accuracy remains within 0.43 meters at the 80th percentile of the cumulative distribution function, with stable localization performance even in dynamic scenarios.

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UWB Positioning System with the Support of MEMS Sensors for Indoor and Outdoor Environment

Cited by 9 publications

References 12 publications

Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects

Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects

An indoor positioning system based on visible light and multi-sensor fusion

Indoor localization algorithm based on geometric deep learning

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