An Application of Clustering and Speed Discrimination to Tracking,

Abstract

A tracking algorithm that combines clustering and speed discrimination is examined. A performance matrix is defined, and for simulated data, the algorithm is shown to have a success rate as high as 92%, in terms of its ability to extract emitter tracks from data. Conditions under which the algorithm succeeds or fails are analyzed. The algorithm is applied to real inorganic sensor data for ships and aircraft, and the results are discussed.

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Document Details

Document Type
Technical Report
Publication Date
Dec 29, 1995
Accession Number
ADA303374

Entities

People

  • Andy Huynh
  • James F. Smith
  • Moon W. Kim

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Sensors

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Clustering
  • Discrimination
  • Mathematics
  • Social Problems

Readers

  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.
  • Theoretical Analysis.