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Signals, Systems and Application of Sensors and Sensing Technologies in Geodesy

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

Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 1377

Special Issue Editors


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Guest Editor
Lassonde School of Engineering, York University, Toronto, ON, Canada
Interests: geodesy; geodynamics; gravity field; gravity space missions; signals and systems; data analytics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Lassonde School of Engineering, York University, Toronto, ON, Canada
Interests: multisensor integrated kinematic positioning and navigation; applied geodesy; data analytics; mobile mapping

Special Issue Information

Dear Colleagues,

The Global Geodetic Observing System (GGOS) advances the knowledge of Earth’s dynamic systems by quantifying our planet’s changes in space and time. The aim is to better understand global change phenomena in order to make informed decisions that will determine the well-being of future generations. This ‘just cause’ requires the continuous deployment of observing platforms, primarily airborne and spaceborne, that collect an unprecedented amount of measurements using different types of sensors. Advancements in fundamental geodetic research and innovative modeling now need validation using advanced data analytics methods. The measurements keep piling up and are waiting to be exploited to achieve the ‘just cause’ of GGOS. This requires novel signal processing methods to be developed. However, the analysis, integration and interpretation of the raw measurements are sometimes inadequate to isolate minute signals buried in noise many orders of magnitude larger. To achieve insightful and meaningful results, we need to enhance the signal processing methodologies in Geodesy, always approaching them from the perspective of systems and physical processes. This Special Issue of Sensors aims to emphasize advances in the development, enhancement, integration, testing, and validation of signals, and image processing methods and algorithms for all types of sensors and measurements. Theoretical, experimental and review papers are strongly encouraged, with no restriction on their length.

Topics include, but are not limited to:

  • Optimal signal processing.
  • Emerging applications of signal and information processing.
  • Radar signal processing.
  • Remote sensing processing.
  • Spectral and wavelet analyses of signals and systems.
  • Sensor measurement fusion and integration
  • Sensor error modeling and calibration.
  • Advanced sensor characterization.
  • Metrology and standards.
  • Novel multisensor integration strategy.
  • Sensor programming.
  • Comprehensive error analysis in Discrete Kalman Filtering.
  • Robust and Adaptive Filtering Algorithms.
  • Indoor georeferencing technology.

Prof. Dr. Spiros Pagiatakis
Prof. Dr. Jianguo Wang
Guest Editors

Manuscript Submission Information

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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.

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Keywords

  • signals and systems
  • sensors
  • filtering
  • spectral and wavelet analyses
  • fusion
  • error analysis
  • sensor systems
  • calibration
  • metrology
  • standards

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Published Papers (1 paper)

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Research

24 pages, 6400 KiB  
Article
Innovative Modeling of IMU Arrays Under the Generic Multi-Sensor Integration Strategy
by Benjamin Brunson, Jianguo Wang and Wenbo Ma
Sensors 2024, 24(23), 7754; https://doi.org/10.3390/s24237754 - 4 Dec 2024
Viewed by 922
Abstract
This research proposes a novel modeling method for integrating IMU arrays into multi-sensor kinematic positioning/navigation systems. This method characterizes sensor errors (biases/scale factor errors) for each IMU in an IMU array, leveraging the novel Generic Multisensor Integration Strategy (GMIS) and the framework for [...] Read more.
This research proposes a novel modeling method for integrating IMU arrays into multi-sensor kinematic positioning/navigation systems. This method characterizes sensor errors (biases/scale factor errors) for each IMU in an IMU array, leveraging the novel Generic Multisensor Integration Strategy (GMIS) and the framework for comprehensive error analysis in Discrete Kalman filtering developed through the authors’ previous research. This work enables the time-varying estimation of all individual sensor errors for an IMU array, as well as rigorous fault detection and exclusion for outlying measurements from all constituent sensors. This research explores the feasibility of applying Variance Component Estimation (VCE) to IMU array data, using separate variance components to characterize the performance of each IMU’s gyroscopes and accelerometers. This analysis is only made possible by directly modeling IMU inertial measurements under the GMIS. A real land-vehicle kinematic dataset was used to demonstrate the proposed technique. The a posteriori positioning/attitude standard deviations were compared between multi-IMU and single IMU solutions, with the multi-IMU solution providing an average accuracy improvement of ca. 14–16% in the estimated position, 30% in the estimated roll and pitch, and 40% in the estimated heading. The results of this research demonstrate that IMUs in an array do not generally exhibit homogeneous behavior, even when using the same model of tactical-grade MEMS IMU. Furthermore, VCE was used to compare the performance of three IMU sensors, which is not possible under other IMU array data fusion techniques. This research lays the groundwork for the future evaluation of IMU array sensor configurations. Full article
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