Adaptive Filtering for Single Target Tracking

Abstract

Many algorithms may be applied to solve the target tracking problem, including the Kalman Filter and different types of nonlinear filters, such as the Extended Kalman Filter (EKF), Unscented Kalman Filter (PF). This paper describes an intelligent algorithm that was developed to elegantly select the appropriate filtering technique depending on the problem and the scenario, based upon a sliding window of the Normalized Innovation Squared (NIS). This technique shows promise for the single target, single radar tracking problem domain. Future work is planned to expand the use of this technique to multiple targets and multiple sensors.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2009
Accession Number
ADA507663

Entities

People

  • Adnan Bubalo
  • Eric W. Jones
  • Gregory Horvath
  • Maria Scalzo
  • Mark Alford
  • Pramod Varshney
  • Ruixin Niu

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Air Force Research Laboratories
  • Algorithms
  • Computational Complexity
  • Computer Science
  • Data Science
  • Detectors
  • Filters
  • Filtration
  • Kalman Filters
  • Mathematical Filters
  • Monte Carlo Method
  • Sequential Monte Carlo Methods
  • Simulations
  • Statistical Algorithms
  • Target Tracking
  • Targets

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.