Combined Unscented Kalman and Particle Filtering for Tracking Closely Spaced Objects

Abstract

Tracking closely space objects with resolution limited sensors is a difficult problem. One way to address this issue is to track these targets individually, and employ relatively complex data association approaches as a means of pairing detections and tracks. The algorithms outlined in this paper takes a different approach, and instead estimates the group velocity using an unscented Kalman Filter (UKF). The UKF state estimate is then employed within a particle filter, which estimates the distribution of objects within the group. It is shown that this approach can be very effective, especially for groups of irregularly spaced objects.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2006
Accession Number
ADA521607

Entities

People

  • Robert J. Pawlak

Organizations

  • Naval Surface Warfare Center

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Cartesian Coordinates
  • Detection
  • Detectors
  • Filters
  • Filtration
  • Kalman Filters
  • Particles
  • Probability
  • Probability Density Functions
  • Radar
  • Random Variables
  • Search Radar
  • Sequential Monte Carlo Methods
  • Targets
  • Two Dimensional

Fields of Study

  • Engineering

Readers

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

Technology Areas

  • Space
  • Space - Space Objects