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        Sensor fusion for boost phase interception of ballistic missiles

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        Author
        Humali, I. Gokhan
        Date
        2004
        Advisor
        Pace, Phillip E.
        Tummala, Murali
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        Abstract
        In the boost phase interception of ballistic missiles, determining the exact position of a ballistic missile has a significant importance. Several sensors are used to detect and track the missile. These sensors differ from each other in many different aspects. The outputs of radars give range, elevation and azimuth information of the target while space based infrared sensors give elevation and azimuth information. These outputs have to be combined (fused) achieve better position information for the missile. The architecture that is used in this thesis is decision level fusion architecture. This thesis examines four algorithms to fuse the results of radar sensors and space based infrared sensors. An averaging technique, a weighted averaging technique, a Kalman filtering approach and a Bayesian technique are compared. The ballistic missile boost phase segment and the sensors are modeled in MATLAB. The missile vector and dynamics are based upon Newton's laws and the simulation uses an earth-centered coordinate system. The Bayesian algorithm has the best performance resulting in a rms missile position error of less than 20 m.
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        http://hdl.handle.net/10945/1408
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        • 1. Thesis and Dissertation Collection, all items
        • Systems Engineering Technical Reports

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          Efficient multiple hypothesis track processing of boost-phase ballistic missiles using IMPULSE-generated threat models 

          Rakdham, Bert (Monterey California. Naval Postgraduate School, 2006-09);
          In this thesis, a multiple hypotheses tracking (MHT) algorithm is developed to successfully track multiple ballistic missiles within the boost phase. The success of previous work on the MHT algorithm and its application ...
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