Gaussian Mixture Reduction for Tracking Multiple Maneuvering Targets in Clutter

Abstract

The problem of tracking multiple maneuvering targets in clutter naturally leads to a Gaussian mixture representation of the Provability Density Function (PDF) of the target state vector. State-of-the-art Multiple Hypothesis Tracking (MHT) techniques maintain the mean, covariance and probability weight corresponding to each hypothesis, yet they rely on ad hoc merging and pruning rules to control the growth of hypotheses.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2003
Accession Number
ADA415317

Entities

People

  • Jason L. Williams

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Advanced Electronics
  • Electronic Warfare
  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Computational Complexity
  • Computational Science
  • Computer Programs
  • Data Science
  • Kalman Filters
  • Mathematical Filters
  • Monte Carlo Method
  • Multiple Hypothesis Tracking
  • Multitarget Tracking
  • Probabilistic Models
  • Probability
  • Probability Density Functions
  • Radar
  • Statistical Algorithms
  • Target Tracking

Readers

  • Sensor Fusion and Tracking Systems.
  • Statistical inference.