Approximate Nonlinear Filtering and Its Applications for GPS

Abstract

In this report we address the problem of nonlinear filtering in the presence of integer uncertainty. In the simulation results we show that particle filtering is capable of resolving integer ambiguity in the given nonlinear setup. Motivated by these results we introduce particle filtering for an exponential family of densities. We prove that under certain conditions the approximated conditional density converges to the true conditional density. For the case where the conditional density does not lie in an exponential family but stays close to it, we show that under certain assumptions the error of the estimate given by this approximate nonlinear filtering, projection particle filtering, is bounded. In the simulation results we show the application of particle filtering to Global Position System (GPS).

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2000
Accession Number
ADA439470

Entities

People

  • Babak Azimi-sadjadi
  • P.S.Krishnaprasad

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Dead Reckoning
  • Differential Equations
  • Equations
  • Filters
  • Filtration
  • Fokker Planck Equations
  • Heuristic Methods
  • Inertial Navigation
  • Inertial Navigation Systems
  • Kalman Filters
  • Mathematical Filters
  • Monte Carlo Method
  • Probability
  • Probability Distributions
  • Random Variables
  • Stochastic Processes

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Fluid Dynamics (CFD)
  • Statistical inference.

Technology Areas

  • Space