• DocumentCode
    2982978
  • Title

    Kalman filter residual expert system

  • Author

    Grimshaw, Captain Jeff ; Amburn, Major Phil

  • Author_Institution
    AFIT/ENG, Wright-Patterson AFB, OH, USA
  • fYear
    1988
  • fDate
    23-27 May 1988
  • Firstpage
    360
  • Abstract
    The Pilot´s Associate (PA) program has been initiated to help mitigate the extensive workload of the fighter pilot. The PA must continually monitor and evaluate important aircraft, weapon, and threat systems as well as terrain and weather conditions by means of sensor systems. The data from these systems must be fused together to present the PA with a coherent picture of the environment. One common technique for fusing sensor data uses Kalman filters in a multiple model adaptive filter (MMAF). An improved filter selection technique is presented as part of an advanced MMAF. A knowledge-based system is used to augment the usual selection technique. Preliminary results indicate that this approach helps in situations that are known to cause problems for Kalman filter-based MMAF systems
  • Keywords
    Kalman filters; aerospace computing; computerised signal processing; expert systems; military computing; Kalman filters; Pilot´s associate program; aircraft; computerised signal processing; fighter pilot; knowledge-based system; multiple model adaptive filter; residual expert system; sensor systems; threat systems; weapon; weather; Aircraft navigation; Artificial intelligence; Condition monitoring; Expert systems; Filters; Intelligent sensors; Phased arrays; Sensor arrays; Sensor fusion; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Aerospace and Electronics Conference, 1988. NAECON 1988., Proceedings of the IEEE 1988 National
  • Conference_Location
    Dayton, OH
  • Type

    conf

  • DOI
    10.1109/NAECON.1988.195037
  • Filename
    195037