Out-of-Sequence Measurements Filtering Using Forward Prediction

Abstract

In target tracking applications, there may be situations where measurements from a given target arrive out of sequence at the processing center. This problem is commonly referred to as Out-of-Sequence Measurements (OOSMs). So far, most of the existing solutions to this problem are based on retrodiction, where backward prediction of the current estimated state is used to incorporate the OOSMs at appropriate time instants. This paper suggests a new method for tackling the OOSMs problem without backward prediction. Based on a forward prediction and de-correlation approach, the method has proved to compare favorably to the best retrodiction-based methods, while requiring less data storage in most cases.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2007
Accession Number
ADA640061

Entities

People

  • A. Benaskeur
  • F. Rheaume

Organizations

  • DRDC Valcartier

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Canada
  • Cartesian Coordinates
  • Classification
  • Computational Complexity
  • Data Association
  • Data Storage Systems
  • Engineering
  • Equations
  • Kalman Filters
  • Knowledge Management
  • Models
  • Multitarget Tracking
  • Nova Scotia
  • Target Tracking
  • Time Intervals

Readers

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