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Scalable Distributed State Estimation in UTM Context.

Marco Cicala1, Egidio D'Amato2, Immacolata Notaro3

  • 1On-board Systems and ATM Department, Italian Aerospace Research Centre CIRA, 81043 Capua (CE), Italy.

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Summary

This study introduces a new method for distributed state estimation in Unmanned Aircraft Systems Traffic Management (UTM) environments. The algorithm improves accuracy by breaking down problems and using local data for cooperative UAVs.

Keywords:
UAS traffic managementconsensus theorydistributed state estimationmultiple UAV navigationnavigation in GPS/GNSS-denied environments

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Area of Science:

  • Robotics and Control Systems
  • Aerospace Engineering
  • Networked Systems

Background:

  • The Unmanned Aircraft Systems Traffic Management (UTM) context presents unique challenges for distributed state estimation (DSE) due to high traffic density and limited communication range.
  • Cooperating Unmanned Aerial Vehicles (UAVs) require robust methods for accurate state estimation (position and velocity) in dynamic, decentralized environments.

Purpose of the Study:

  • To propose a novel algorithm for Distributed State Estimation (DSE) tailored for cooperative UAVs within a UAS Traffic Management (UTM) framework.
  • To enhance the accuracy and scalability of state estimation by leveraging heterogeneous sensors and Vehicle-to-Vehicle (V2V) communication.

Main Methods:

  • A scalable, decentralized Kalman Filter algorithm derived from Internodal Transformation Theory and enhanced with Consensus Theory.
  • A self-organizing approach where local estimation nodes adapt to time-varying communication topologies.
  • Integration of on-board sensor measurements with transmitted information from neighboring vehicles.

Main Results:

  • The algorithm effectively reduces the complex DSE problem into smaller, manageable local sub-problems.
  • Exploitation of nearby measurements significantly improves the accuracy of local state estimates for individual UAVs.
  • Demonstrated capability for each UAV to estimate its own state and those of nearby vehicles within a simulated UTM scenario.

Conclusions:

  • The proposed DSE algorithm offers a scalable and accurate solution for cooperative UAVs in UTM environments.
  • The method effectively balances computational load and enhances estimation precision through decentralized processing and data fusion.
  • This approach facilitates improved situational awareness and cooperative behavior crucial for safe and efficient UAS operations.