The collision avoidance system that is currently deployed worldwide relies upon radar beacon surveillance. With its broad deployment over the next decade, aviation surveillance based on Automatic Dependent Surveillance-Broadcast (ADS-B) reports may reduce the need for frequent beacon interrogation over the communication channel, but there is a risk of ADS-B providing erroneous data to the collision avoidance system, resulting in a potential collision. Hence, there is a need to use beacon interrogation to periodically validate ADS-B position reports. Various threshold-based validation strategies based on proximity and closure rate have been suggested to reduce channel congestion while maintaining the reliability of the collision avoidance system. This paper shows how to model the problem of deciding when to validate ADS-B reports as a partially observable Markov decision process, and it explains how to solve for the optimal validation strategy. The effectiveness of this approach is demonstrated in simulation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Position validation strategies using partially observable Markov decision processes




    Publication date :

    2011-10-01


    Size :

    372147 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Signal Source Localization Using Partially Observable Markov Decision Processes

    Dressel, Louis K. / Kochenderfer, Mykel J. | AIAA | 2015



    Multirobot Navigation Using Partially Observable Markov Decision Processes with Belief-Based Rewards

    Tzikas, Alexandros E. / Knowles, Derek / Gao, Grace X. et al. | AIAA | 2023


    Signal Source Localization Using Partially Observable Markov Decision Processes (AIAA 2015-0116)

    Dressel, Louis K. / Kochenderfer, Mykel J. | British Library Conference Proceedings | 2015


    Health-Aware Multi-UAV Planning using Decentralized Partially Observable Semi-Markov Decision Processes

    Omidshafiei, Shayegan / Agha-mohammadi, Ali-akbar / Amato, Christopher et al. | AIAA | 2016