Uncertainty Bounds for State Estimates with Applications in Target Tracking

Abstract

This work considers the application of the extended Kalman filter (EKF) in systems with nonlinear state transition equations. We develop a surrogate-based method to approximate the uncertainty bounds of the EKF using only one trajectory, without the need to simulate many independent replicates. This method is demonstrated in a target tracking problem in 3D space.

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Document Details

Document Type
Technical Report
Publication Date
Apr 11, 2023
Accession Number
AD1200988

Entities

People

  • James C. Spall
  • Shihong Wei

Organizations

  • Johns Hopkins University

Tags

Communities of Interest

  • Air Platforms
  • Materials and Manufacturing Processes
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Applied Mathematics
  • Data Science
  • Equations
  • Filters
  • Information Processing
  • Information Science
  • Kalman Filters
  • Mathematical Filters
  • Nonlinear Systems
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Algorithms
  • Statistical Tests
  • Target Tracking
  • Three Dimensional
  • Unmanned Aerial Vehicles

Fields of Study

  • Engineering

Readers

  • Calculus or Mathematical Analysis
  • Computational Modeling and Simulation
  • Positioning, Navigation, and Timing (PNT) Technology.

Technology Areas

  • Space