Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Advances of Navigation, Positioning, Monitoring and Predicting Based on Inertial...
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances of Navigation, Positioning, Monitoring and Predicting Based on Inertial Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (10 February 2024) | Viewed by 6549

Special Issue Editors

Prof. Dr. Rosario Schiano Lo Moriello

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Naples Federico II, 80138 Napoli, NA, Italy
Interests: augmented reality for remote educational applications; augmented reality for remote control of instrumentation and systems; Internet of Thing-based monitoring and measurement platforms; advanced sampling strategies for embedded measurement systems; measurement methods for IoT-oriented communication protocol and systems; methods for error compensation of MEMS-based inertial measurement units
Special Issues, Collections and Topics in MDPI journals
Dr. Giorgio De Alteriis

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80138 Napoli, NA, Italy
Interests: mechanical and thermal measurements; MEMS sensors; inertial measurement unit; integrated navigation systems; drone; kalman filter; IoT platforms

Special Issue Information

Dear Colleagues,

Inertial sensors, particularly those realized in MEMS technology, have now reached such technological maturity that they are suitable for a multitude of applications, ranging from localization (both indoor and outdoor) to navigation (mainly integrated), from continuous monitoring of components and systems to predicting their operating conditions and health.

On the other hand, despite the satisfactory achieved performance, such sensors still suffer from the typical problems of accelerometers and gyroscopes, such as bias stability and instability, thermal drift, scale factor, misalignment, and crosstalk, which limit their deployment for the most challenging applications. It is thus desirable to integrate the outputs of such sensors with information from other sources in order to overcome the aforementioned limitations or to develop new compensation techniques to further reduce their impact on measurement quality.

This Special Issue aims, therefore, to collect the best and most innovative proposals on the topic, thus enabling a wide and fast dissemination of findings among different scholars. Papers that present innovation from a methodological or application point of view will be considered in addition, of course, to reviews concerning both the topics and keywords indicated below:

  • MEMS inertial sensors
  • MEMS environment sensors
  • MEMS calibrations
  • MEMS characterizations
  • Inertial navigation applications
  • Indoor and outdoor localization
  • Monitoring systems
  • AI-based solutions
  • Predictive maintenance
  • IoT applications

Prof. Dr. Rosario Schiano Lo Moriello
Dr. Giorgio de Alteriis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 16956 KiB  
Article
RIOT: Recursive Inertial Odometry Transformer for Localisation from Low-Cost IMU Measurements
by James Brotchie, Wenchao Li, Andrew D. Greentree and Allison Kealy
Sensors 2023, 23(6), 3217; https://doi.org/10.3390/s23063217 - 17 Mar 2023
Cited by 6 | Viewed by 2354
Abstract
Inertial localisation is an important technique as it enables ego-motion estimation in conditions where external observers are unavailable. However, low-cost inertial sensors are inherently corrupted by bias and noise, which lead to unbound errors, making straight integration for position intractable. Traditional mathematical approaches [...] Read more.
Inertial localisation is an important technique as it enables ego-motion estimation in conditions where external observers are unavailable. However, low-cost inertial sensors are inherently corrupted by bias and noise, which lead to unbound errors, making straight integration for position intractable. Traditional mathematical approaches are reliant on prior system knowledge, geometric theories and are constrained by predefined dynamics. Recent advances in deep learning, which benefit from ever-increasing volumes of data and computational power, allow for data-driven solutions that offer more comprehensive understanding. Existing deep inertial odometry solutions rely on estimating the latent states, such as velocity, or are dependent on fixed-sensor positions and periodic motion patterns. In this work, we propose taking the traditional state estimation recursive methodology and applying it in the deep learning domain. Our approach, which incorporates the true position priors in the training process, is trained on inertial measurements and ground truth displacement data, allowing recursion and learning both motion characteristics and systemic error bias and drift. We present two end-to-end frameworks for pose invariant deep inertial odometry that utilises self-attention to capture both spatial features and long-range dependencies in inertial data. We evaluate our approaches against a custom 2-layer Gated Recurrent Unit, trained in the same manner on the same data, and tested each approach on a number of different users, devices and activities. Each network had a sequence length weighted relative trajectory error mean ≤0.4594 m, highlighting the effectiveness of our learning process used in the development of the models. Full article
(This article belongs to the Special Issue Advances of Navigation, Positioning, Monitoring and Predicting Based on Inertial Sensors)
►▼ Show Figures

Figure 1

16 pages, 930 KiB  
Article
Turntable IMU Calibration Algorithm Based on the Fourier Transform Technique
by Yury Bolotin and Vladimir Savin
Sensors 2023, 23(2), 1045; https://doi.org/10.3390/s23021045 - 16 Jan 2023
Cited by 2 | Viewed by 2471
Abstract
The paper suggests a new approach to calibration of a micromechanical inertial measurement unit. The data are collected on a simple rotating turntable with horizontal (or close to) rotation axis. For such a turntable, an electric screwdriver with fairly low rotation rate can [...] Read more.
The paper suggests a new approach to calibration of a micromechanical inertial measurement unit. The data are collected on a simple rotating turntable with horizontal (or close to) rotation axis. For such a turntable, an electric screwdriver with fairly low rotation rate can be used. The algorithm is based on the Fourier transform applied to the rotation experimental data, implemented as FFT. The frequencies and amplitudes of the spectral peaks are calculated and collected in a small set of data, and calibration is done explicitly with these data. Calibration of an accelerometer triad and choosing the IMU coordinate frame are reduced to approximating the collected data with an ellipsoid in three dimensions. With rotation frequency calculated as the peak frequency of accelerometer readings, calibration of the gyros is a straightforward linear least square problem. The algorithm is purely algebraic, requires no iterations and no initial guess on the parameters, and thus encounters no convergence problems. The algorithm was tested both with simulated and experimental data, with some promising results. Full article
(This article belongs to the Special Issue Advances of Navigation, Positioning, Monitoring and Predicting Based on Inertial Sensors)
►▼ Show Figures

Figure 1

22 pages, 8615 KiB  
Article
Adaptive Data Transmission Algorithm for the System of Inertial Sensors for Hand Movement Acquisition
by Michał Pielka, Paweł Janik, Małgorzata A. Janik and Zygmunt Wróbel
Sensors 2022, 22(24), 9866; https://doi.org/10.3390/s22249866 - 15 Dec 2022
Viewed by 1180
Abstract
Modern systems of intelligent sensors commonly use radio data transmission. Hand movement acquisition with the use of inertial sensors requires the processing and transmission of a relatively large amount of data, which may be associated with a significant load on the network structure. [...] Read more.
Modern systems of intelligent sensors commonly use radio data transmission. Hand movement acquisition with the use of inertial sensors requires the processing and transmission of a relatively large amount of data, which may be associated with a significant load on the network structure. Network traffic limitation, without losing the quality of monitoring parameters from the sensor system, is therefore important for the functioning of the radio network which integrates both the teletransmission sensor system and the data acquisition server. The paper presents a wearable solution for hand movement acquisition, which uses data transmission in the Wi-Fi standard and contains 16 MEMS (Micro Electro Mechanical System) sensors. An adaptive algorithm to control radio data transmission for the sensor system has been proposed. The algorithm implemented in the embedded system controls the change of the frame length, the length of the transmission frame and the frequency of its sending, which reduces the load on the network router. The use of the algorithm makes it possible to reduce the power consumption by the sensor system by up to 19.9% and to limit the number of data transferred by up to about 91.6%, without losing the quality of the monitored signal. The data analysis showed no statistically significant differences (p > 0.05) between the signal reconstructed from the complete data and processed by the algorithm. Full article
(This article belongs to the Special Issue Advances of Navigation, Positioning, Monitoring and Predicting Based on Inertial Sensors)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop