Tracking of Multiple Maneuvering Targets Using Multiscan JPDA and IMM Filtering

Abstract

We consider the problem of tracking multiple maneuvering targets in the presence of clutter using switching multiple target motion models. A novel suboptimal filtering algorithm is developed by applying the basic interacting multiple model (IMM) approach and joint probability data association technique. But unlike the standard single scan joint probabilistic data association (JPDA) approach, we exploit a multiscan joint probabilistic data association (Mscan-JPDA) approach to solve the data association problem. The algorithm is illustrated via a simulation example involving training of three maneuvering targets and a multiscan data window of length two.

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Document Details

Document Type
Technical Report
Publication Date
Sep 12, 2003
Accession Number
ADA417567

Entities

People

  • Jitendra K. Tugnait
  • Sumedh Puranik

Organizations

  • Auburn University

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Cartesian Coordinates
  • Computational Science
  • Computer Simulations
  • Covariance
  • Data Association
  • Data Processing
  • Data Science
  • Detectors
  • Information Science
  • Multiple Targets
  • Multitarget Tracking
  • Probability
  • Random Variables
  • Simulations
  • Standards
  • Target Tracking

Fields of Study

  • Engineering

Readers

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