UWB/IMU Integration with Adaptive Motion Constraints to Support UXO Mapping

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

UWB/IMU Integration with Adaptive Motion Constraints to Support UXO Mapping

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

UWB/IMU Integration with Adaptive Motion Constraints to Support UXO Mapping

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the ION 2017 Pacific PNT Meeting

ISBN

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ISSN

2329-2849

e-ISSN

2329-2849

Počet stran výsledku

10

Strana od-do

429-438

Název nakladatele

Institute of Navigation

Místo vydání

Fairfax

Místo konání akce

Honolulu

Datum konání akce

1. 5. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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