Consideration of Particle Flow Filter Implementations and Biases

Abstract

Particle flow filters are appealing due to their potential resistance to particle collapse. However, common implementations exhibit undesirable biases or particle divergence. This paper shows that the explicit, incompressible, and diagonal flows, unlike the Gromov flow, are inherently biased. Another issue is errors in the numerical integration of the flow. The benefits of implicit stochastic-integration methods are demonstrated and a new adaptive step-size selection heuristic is presented.

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Document Details

Document Type
Technical Report
Publication Date
Feb 11, 2020
Accession Number
AD1091426

Entities

People

  • Codie Lewis
  • David F. Crouse

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Sensors
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Bayes Theorem
  • Cartesian Coordinates
  • Collapse
  • Coordinate Systems
  • Covariance
  • Data Processing
  • Data Science
  • Decomposition
  • Differential Equations
  • Equations
  • Filters
  • Filtration
  • Fokker Planck Equations
  • Gaussian Distributions
  • Gaussian Noise
  • Incompressible Flow
  • Information Science
  • Kalman Filters
  • Mathematical Filters
  • Military Research
  • Partial Differential Equations
  • Probability Density Functions
  • Random Variables
  • Sequential Monte Carlo Methods
  • Square Roots
  • Theorems

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Combustion and Flow Dynamics.
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)