A hybrid statistical technique for modeling recurrent tracks in a compact set

Christopher Griffin, Richard R. Brooks, Jason Schwier

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Abstract

In this technical note we present a hybrid statistical approach for modeling a vehicle's behavior as it traverses a compact set in Euclidean space. We use Symbolic Transfer Functions (STF), developed by the authors for modeling stochastic input/output systems whose inputs and outputs are both purely symbolic. We apply STF to our problem by assuming that the input symbols represent regions of space through which a track is passing while the output represents specific linear functions that more precisely model the behavior of the track. A target's behavior is modeled at two levels of precision: The symbolic model provides a probability distribution on the next region of space and behavior (linear function) that a vehicle will execute, while the continuous model predicts the position of the vehicle using classical statistical methods. The following results are presented: (i) An algorithm that parsimoniously partitions the space of the vehicle and models the behavior in the partitions with linear functions. (ii) A demonstration of our approach using real-world ship track data.

Original languageEnglish (US)
Article number5742767
Pages (from-to)1926-1931
Number of pages6
JournalIEEE Transactions on Automatic Control
Volume56
Issue number8
DOIs
StatePublished - Aug 1 2011

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All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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