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APOS-UE

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APOS-UE - sistema de posicionamiento de soldados en entornos urbanos con señal GNSS denegada APOS-UE - sistema de posicionamiento de soldados en entornos urbanos con señal GNSS denegada
APOS-UE - sistema de posicionamiento de soldados en entornos urbanos con señal GNSS denegada
APOS-UE - sistema de posicionamiento de soldados en entornos urbanos con señal GNSS denegada
  • Client: EDA (European Defence Agency)
  • Country: Belgium
  • Start date: 2018
  • Ending date: 2021
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APOS-UE is a Category B project being developed jointly in Italy, France and Spain for the EDA. It is intended to provide a positioning system for soldiers in GNSS-denied urban environments, including navigating both inside and outside buildings.

The project relies on the incorporation of four innovative navigation technologies, a system for fusing information that helps to improve technology-based localization performance, and a device for displaying critical information.

In this project, SENER is responsible for developing the algorithms that synchronize and fuse the data generated by the various technologies. In addition, a 3D simulation environment has been created that can reproduce the actual test scenarios, which allows us to anticipate problems during the technological development.

The project is based on ROS and Gazebo, which rely on open-source tools, thereby allowing for faster development times.

As part of the project's data fusion process, two classic solutions based on Kalman filters have been implemented (EKF and UKF), along with a novel hybrid algorithm (SKPF), resulting from the combination of a Kalman UKF filter and a particle filter (PF). In addition to the tasks of fusing the data required by the project, the SKPF hybrid algorithm was also validated by analyzing its advantages, disadvantages and ideal use scenarios.

The 3D simulation environment developed allows taking different paths commanded by the user and simulates the data from the four technologies involved in the project, whose errors have been modelled using various statistical tools and probability distributions. The result makes it possible to execute different fusion filters, analyze their results, draw conclusions and iterate the design of the algorithm.

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