Kalman Filtering with Nonlinear State Constraints

Abstract

In [Simon and Chia, 2002], an analytic method was developed to incorporate linear state equality constraints into the Kalman filter. When the state constraint is nonlinear, linearization was employed to obtain an approximately linear constraint around the current state estimate. This linearized constrained Kalman filter is subject to approximation errors and may suffer from a lack of convergence. In this paper, we present a method that allows exact use of second order nonlinear state constraints. It is based on a computational algorithm that iteratively finds the Lagrangian multiplier for the nonlinear constraints. The method therefore provides better approximation when higher order nonlinearities are encountered. Computer simulation results are presented to illustrate the algorithm.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2006
Accession Number
ADA521137

Entities

People

  • Chun Yang
  • Erik Blasch

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Coordinate Systems
  • Data Science
  • Estimators
  • Filters
  • Filtration
  • Geometry
  • Information Science
  • Kalman Filtering
  • Kalman Filters
  • Mathematical Analysis
  • Mathematical Filters
  • Measurement
  • Optimal Estimators
  • Simulations
  • Statistical Analysis
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Operations Research