Fine-grained acoustic positioning with compensation of CDMA interference

Seco, Fernando; Jiménez, Antonio R.; Zampella, Francisco

doi:10.1109/icit.2015.7125606

Cited by 13 publications

(10 citation statements)

References 13 publications

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“…In indoor environments, where GNSS signals degrade too much to be reliable, other technologies have been explored for more than 20 years to get robust and accurate Indoor Positioning Systems (IPSs). Despite the good accuracy achieved by some systems, such as those based on sub-meter Ultra Wide Band (UWB) positioning [15,16], Ultrasound [17] and Visible Light [18], they still present barriers in their adoption due to costs for additional equipment and difficulties in deploying and maintaining dedicated infrastructure. Furthermore, there is the need to get reliable indoor positioning information with commodity devices, such as smart phone with embedded sensors and radio interfaces.…”

Section: Related Workmentioning

confidence: 99%

Indoor Bluetooth Low Energy Dataset for Localization, Tracking, Occupancy, and Social Interaction

Baronti

Chessa

Mavilia

et al. 2018

Sensors

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Indoor localization has become a mature research area, but further scientific developments are limited due to the lack of open datasets and corresponding frameworks suitable to compare and evaluate specialized localization solutions. Although several competitions provide datasets and environments for comparing different solutions, they hardly consider novel technologies such as Bluetooth Low Energy (BLE), which is gaining more and more importance in indoor localization due to its wide availability in personal and environmental devices and to its low costs and flexibility. This paper contributes to cover this gap by: (i) presenting a new indoor BLE dataset; (ii) reviewing several, meaningful use cases in different application scenarios; and (iii) discussing alternative uses of the dataset in the evaluation of different positioning and navigation applications, namely localization, tracking, occupancy and social interaction.

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Section: Related Workmentioning

confidence: 99%

Indoor Bluetooth Low Energy Dataset for Localization, Tracking, Occupancy, and Social Interaction

Baronti

Chessa

Mavilia

et al. 2018

Sensors

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Section: Introductionmentioning

confidence: 99%

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Mendoza-Silva

Richter

Torres-Sospedra

et al. 2018

Data

133

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Abstract:WiFi fingerprinting, one of the most popular methods employed in indoor positioning, currently faces two major problems: lack of robustness to short and long time signal changes and difficult reproducibility of new methods presented in the relevant literature. This paper presents a WiFi RSS (Received Signal Strength) database created to foster and ease research works that address the above-mentioned two problems. A trained professional took several consecutive fingerprints while standing at specific positions and facing specific directions. The consecutive fingerprints may enable the study of short-term signals variations. The data collection spanned over 15 months, and, for each month, one type of training datasets and five types of test datasets were collected. The measurements of a dataset type (training or test) were taken at the same positions and directions every month, in order to enable the analysis of long-term signal variations. The database is provided with supporting materials and software, which give more information about the collection environment and eases the database utilization, respectively. The WiFi measurements and the supporting materials are available at the Zenodo repository under the open-source MIT license.

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“…However, none of these technologies is made to provide precise ranging measurements and force the IPSs based on it to rely on creating large fingerprint database to match incoming positioning requests, or received signal strength (RSS) to distance conversions in order to locate a target. On the other hand, the most accurate beacon based IPS solutions are the ones using ultrasounds [11,12,13] or UWB radio signals [14,15,16]. However, ultrasounds have the significant drawback of very limited maximum range and may not penetrate obstacles and have rather high-power consumption.…”

Section: Overview and Motivationsmentioning

confidence: 99%

Practical And Robust Approach For A Neural Networks Based Indoor Positioning System Using Ultrawide Band

Murari¹

2021

Preprint

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With the increasing widespread of sensor technology, new solutions for indoor positioning systems are continuously being developed and with them, new services requiring accurate positioning data have seen a great rise in popularity. In this thesis, a new design technique and deployment methodology for an indoor positioning system using neural networks is proposed to offer more flexibility and simplicity in the development of such a system which is currently very context-bound. The usage of battery-powered tags implies also that systems should not require excessive power consumption and the large number of targets to position requires a method that is not only accurate but also scalable. The proposed positioning system utilizes a small “swarm” of neural networks tasked to position targets based on distance measurements from Ultrawide Band sensors and requires shorter fingerprint collection campaigns and enables more flexibility in system deployment and alterations. Instead of relying solely on real data collected on the field for the training of neural networks, synthetic data is used for an initial training phase. Together, these propositions allow flexibility in terms of adding, removing or altering positions of reference nodes and simplifies offline deployment operations of an indoor positioning system. This thesis presents a system operating in a laboratory-workshop environment capable of good positioning accuracies and maintains robust performances in poor signal propagation.

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Fine-grained acoustic positioning with compensation of CDMA interference

Cited by 13 publications

References 13 publications

Indoor Bluetooth Low Energy Dataset for Localization, Tracking, Occupancy, and Social Interaction

Indoor Bluetooth Low Energy Dataset for Localization, Tracking, Occupancy, and Social Interaction

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Practical And Robust Approach For A Neural Networks Based Indoor Positioning System Using Ultrawide Band

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