UWB/IMU Integration with Adaptive Motion Constraints to Support UXO Mapping
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00312357" target="_blank" >RIV/68407700:21230/17:00312357 - isvavai.cz</a>
Result on the web
<a href="https://www.ion.org/publications/abstract.cfm?articleID=15070" target="_blank" >https://www.ion.org/publications/abstract.cfm?articleID=15070</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
UWB/IMU Integration with Adaptive Motion Constraints to Support UXO Mapping
Original language description
A platform equipped with electromagnetic inference (EMI) sensor allows us to map underground areas and searching for unexploded ordnances (UXO). This mapping requires the precise navigation of the platform. In this paper, we use UWB/IMU integration to determine position and attitude of a UXO platform. UWB outages may often occur due to non-line of sight between the UWB network nodes and the rover. To mitigate the errors during short UWB outages, we consider the special dynamics of the platform by adaptively applying constraints in the navigation filter. Typically, a UXO platform moves straight, performs turns or stops; these are the three main dynamic states. Each dynamic state has a set of constraint equations that describes the specific motion. A neural network determines the current dynamic state based on IMU data. Two types of neural networks are examined: (1) a feed-forward network that uses the mean and variance of the IMU data, and (2) a proposed convolution network that takes the raw IMU data as inputs to determine the current dynamic state. The networks are trained on a dataset that was acquired during good GNSS signal reception, and UWB/IMU, GNSS/IMU solutions, whoever, the UWB had some outages. On this dataset, we found that the adaptive constraints mitigate the error of these outage and the UWB/IMU integrated solution by 10% (3-4 cm) using the GNSS/IMU solution as ground truth. The investigation did not show any statistically significant performance difference between the two neural network types.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the ION 2017 Pacific PNT Meeting
ISBN
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ISSN
2329-2849
e-ISSN
2329-2849
Number of pages
10
Pages from-to
429-438
Publisher name
Institute of Navigation
Place of publication
Fairfax
Event location
Honolulu
Event date
May 1, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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