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Multiple-Sensor Discrimination of Closely-Spaced Objects on a Ballistic Trajectory

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Trident Scholar Project rept. no. 439

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One of the challenges associated with defending against ballistic missiles is to discriminate the object of interest among multiple closely-spaced objects CSOs that travel on a ballistic trajectory. The discrimination process typically involves the identification and tracking of the object of interest. One approach that can improve discrimination performance is to employ multiple sensors. Multiple-sensor correlation and discrimination involves the integration of sensor measurements collected from terrestrial and on-orbit sensors to improve the likelihood of identifying and tracking an object of interest within the CSOs. This report describes the development of the algorithms necessary for fusing sensor measurement data obtained from multiple, dissimilar sensors in order to improve the likelihood of identifying and tracking an object of interest within closely-spaced objects traveling on a ballistic trajectory. The algorithms utilize a target object map TOM that is created using multiple sensor measurements for correlation. The object of interest is then selected using a probability-based Dempster-Shafer discrimination algorithm. To examine the performance of these algorithms, a simulation environment was developed. It included relevant characteristics of the sensors in the discrimination system, a modeling process for the ballistic trajectories of the CSOs, and a decision-making module containing the algorithms for handling the sensor returns and correlating and discriminating the object of interest.

Subject Categories:

  • Statistics and Probability
  • Cybernetics
  • Antimissile Defense Systems
  • Optical Detection and Detectors
  • Active and Passive Radar Detection and Equipment

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