Recursive Filtering Algorithms for Ship Tracking

Abstract

Some recursive filtering algorithms were developed for tracking ships when observations are sporadic and imprecise. Tracking with position-only observations was emphasized, but a procedure was also developed for utilizing possible independent observations of ship velocity. Two basic algorithms are considered: a Kalman filter with adaptive driving noise for generating estimates (and containment ellipses) for current and future ship positions, and a corresponding Bayesian smoother for generating estimates of past positions. The driving noise was treated as a velocity term in a continuous-time model of ship's motion. The details of these two algorithms were developed for tracking on a plane, on a sphere in geographical coordinates, and on a sphere in three-dimensional rectilinear coordinates. A FORTRAN implementation and some corresponding numerical results were developed for the planar case.

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Document Details

Document Type
Technical Report
Publication Date
Apr 06, 1976
Accession Number
ADA024329

Entities

People

  • Warren W. Willman

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • C4I
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Algorithms
  • Brownian Motion
  • Cartesian Coordinates
  • Command And Control
  • Computations
  • Coordinate Systems
  • Dead Reckoning
  • Differential Equations
  • Grids
  • Kalman Filters
  • Probability
  • Probability Distributions
  • Random Variables
  • Recursive Filters
  • Rotation
  • Ship Motion
  • Three Dimensional

Readers

  • Approximation Theory.
  • Distributed Systems and Data Platform Development
  • Space Exploration and Orbital Mechanics.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms