ML-PMHT Track Detection Threshold Determination for K-Distributed Clutter

Abstract

Recent work developed a novel method for determining tracking thresholds for the Maximum Likelihood Probabilistic Multi-Hypothesis Tracker (ML-PMHT). Under certain ideal conditions, probability density functions (PDFs) for the peak points in the ML-PMHT log-likelihood ratio (LLR) due to just clutter measurements could be calculated. Analysis of these clutter-induced peak PDFs allowed for the calculation of tracking thresholds, which previously had to be done with time-consuming Monte Carlo simulations. However, this work was done for a very specific case: the amplitudes of both target and clutter measurements followed Rayleigh distributions. The Rayleigh distribution is a very light-tailed distribution, and it can be overly optimistic in predicting that high-SNR measurements are target-originated. This work examines the case where the clutter amplitudes do not follow a Rayleigh distribution at all, but instead follow a K-distribution, which more accurately describes active acoustic clutter. This will provide a framework for determining accurate tracking thresholds for the ML-PMHT algorithm.

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Document Details

Document Type
Technical Report
Publication Date
May 15, 2014
Accession Number
AD1019849

Entities

People

  • Peter Willett
  • Steven Schoenecker
  • Yaakov Bar-Shalom

Organizations

  • University of Connecticut

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Active Sonar
  • Algorithms
  • Amplitude
  • Data Processing
  • Detection
  • Detectors
  • Intensity
  • Measurement
  • Military Research
  • Peak Values
  • Probability
  • Probability Distributions
  • Random Variables
  • Sonar
  • Three Dimensional
  • Two Dimensional

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Sensor Fusion and Tracking Systems.
  • Statistical inference.