Unified Data Fusion Applied to Monopulse Tracking

Abstract

The direction of arrival (DOA) computed from the monopulse ratio is known to fluctuate widely in the presence of multiple unresolved targets. This confounds traditional trackers operating on unresolved targets, leading to erroneous state estimates or loss of track. This paper presents a computationally feasible solution to this problem using Metron s Unified Theory of Data Fusion (UDF). UDF is a Bayesian method that maintains a probability density on the joint target state space. It operates without explicitly enumerating multiple data-totarget associations. This is particularly important for unresolved targets where the data cannot be attributed to a single target. Likelihood functions for two Rayleigh targets over a range of SNRs are examined first to develop insight. The final example presents the application to tracking two low-SNR targets crossing the radar beam.

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Document Details

Document Type
Technical Report
Publication Date
May 25, 1999
Accession Number
ADA394682

Entities

People

  • Michael V. Finn

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Boresights
  • Data Analysis
  • Data Fusion
  • Gaussian Distributions
  • Hypotheses
  • Information Science
  • Markov Processes
  • Measurement
  • Military Research
  • Multitarget Tracking
  • Probability
  • Probability Distributions
  • Random Variables
  • Simulations
  • Target Strength
  • Target Tracking
  • Targets

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Radar Systems Engineering.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space
  • Space - Space Objects