Application of AI and ML to platform alternative PNT for GNSS Degraded Environments (Leonardo)

Research Theme

Sensor Signal Processing 

Aim

To use AI/ML methods to adapt and optimise the PNT solution in the absence of reliable GNSS for varying sensor suites in varying environments, whilst remaining agnostic regarding inputs and platform type.

Objectives

1. Obtain sufficient labelled data, real and synthetic, for PNT from sensor systems in varying platforms and environments to apply Machine Learning (ML) techniques to.
2. Identify suitable AI/ML methods to be applied to the data with realistic metrics as to what constitutes a “good” solution regardless of environmental and sensor suit constraints.
3. Develop the selected methods on the sensor and digital world-model data available to provide system level PNT solutions for varying GNSS conditions and platform types.
4. Validate the methods against independent data with GNSS “truth” available.

Description

Positioning, Navigation and Timing (PNT) without reliable GNSS is a complex problem for which a wide variety of solutions are being developed, from signals of opportunity to mutli-sensor fusion. What methods can be applied vary significantly depending on the platform type and the sensor suite available on that platform and with the environment the sensors and platform operate in. Moreover, the sensor information available may vary even within a mission depending on what other tasks the sensors have to undertake and the environment (physical and electromagnetic) may change over the course of the mission. Ensuring some level of agnosticism enables a solution to maintain a highly accurate output level, giving the AI/ML solution a wider use case. Varying input types and numbers of is to be considered, as to not cause any sensor suit constraints for the wider platform. The information the sensors provide, must also be referenced against the “worldview” that the AI/ML has been trained on e.g. digital terrain models, locations of features, emitters, etc, to provide and alternative to GNSS in a global reference frame. These models can also be used to determine which alternative PNT methods are optimum in specific environments. Building on work begun on other SPADS CDT projects and making use of data available, the project will look at how ML can be used to train a PNT solution to use different data sources over the mission to meet the required accuracy of PNT for the mission to be carried out successfully and to learn from previous missions to improve PNT performance over time. The system will not be concerned with GNSS signal validity and will aim to be ‘activated’ as a primary navigation system upon GNSS interferences. This means the system should be able to operate using all levels of GNSS available, providing the most notable PNT accuracy increase.