Process Noise Selection Based on Mismatched Filter Design

Abstract

This report derives a explicit recursions for the MSE and bias of both predicted and posterior estimates of a constant gain discrete-time Kalman filter when run on an arbitrary mismatched model. For constant maneuvers, asymptotic solutions are also provided. These solutions are then used to optimize over process noise parameters for Kalman filtering to either minimize the MSE of a mismatched model or such that the maximum error of a mismatched model equals the measurement MSE. As compared to similar solutions in previous work, this formulation of the problem allows for explicit solutions to be obtained in certain scenarios and also to optimize over more difficult trajectory types than are traditionally considered. A number of examples, including finding the optimal covariance scale factor for an evasive weaving target in 2D, are considered.

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Document Details

Document Type
Technical Report
Publication Date
Mar 02, 2023
Accession Number
AD1194686

Entities

People

  • David F. Crouse

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Computations
  • Coordinate Systems
  • Detectors
  • Equations
  • Estimators
  • Filtration
  • Heuristic Methods
  • Intervals
  • Kalman Filtering
  • Kalman Filters
  • Monte Carlo Method
  • Navigation
  • Optimization
  • Polynomials
  • Radar Tracking
  • Riccati Equation
  • Simulations
  • Target Tracking
  • Time Intervals
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Calculus or Mathematical Analysis
  • Radio communications and signal processing.
  • Regression Analysis.