Sensor Management and Nonlinear Filtering Research

Abstract

This grant is supporting development of mathematical foundations for sensor management and nonlinear filtering. The accomplishments so far are in two areas: (1) The use of Interactive Multiple Model Kalman Filters (IMMKF) with a metric called discrimination gain (DG); and (2) the use of nonlinear filtering, (NLF) in the tracking of target elevation for objects flying close to a reflecting surface. In the case of IMMKF, we demonstrate, using simulated data, that IMMKF can be used to compute the information gain when multiple sensors observe a collection of maneuvering airborne targets. In the case of NLF, we demonstrate the feasibility of using NLF methods for altitude tracking in multipath.

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Document Details

Document Type
Technical Report
Publication Date
Oct 31, 1998
Accession Number
ADA360450

Entities

People

  • Avner Friedman
  • Keith Kastella
  • Wayne Schmaedeke

Organizations

  • University of Minnesota

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Air Traffic
  • Airborne
  • Algorithms
  • Computational Fluid Dynamics
  • Computational Science
  • Detection
  • Differential Equations
  • Equations
  • Filters
  • Filtration
  • Fokker Planck Equations
  • Mathematical Analysis
  • Mathematical Filters
  • Partial Differential Equations
  • Probability
  • Radar
  • Stochastic Processes

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Data Mining and Knowledge Discovery.
  • Radar Systems Engineering.