Estimation of Aircraft Target Motion using Pattern Recognition Orientation Measurements.

Abstract

A new approach to estimating motion of a highly maneuvering aircraft target in an air-to-air tracking scenario is presented. An interactive filter system is developed which provides an improved estimate of target motion states by conditioning kinematic filter estimates upon target aspect angle data. Pattern recognition techniques used with an electro-optical tracker are presumed to provide this target aspect information. A target orientation filter processes the aspect angle measurements by statistically weighting measured aspect angles with the current best estimate of target kinematics. The aerodynamic lift equation is used to relate approximate angle of attack to target velocity and acceleration. A novel statistical model for aircraft target normal acceleration is also developed to better represent unknown target accelerations. Simulation results of realistic three-dimensional scenarios are presented to evaluate the performance of the interactive filter system. The report contains a 56-item bibliography. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1978
Accession Number
ADA066195

Entities

People

  • Jerry D. Kendrick

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Artificial Intelligence
  • Computational Science
  • Computer Simulations
  • Coordinate Systems
  • Databases
  • Detectors
  • Differential Equations
  • Geometry
  • Inertial Navigation
  • Inertial Navigation Systems
  • Information Science
  • Kalman Filters
  • Mathematical Filters
  • Pattern Recognition
  • Weapon Delivery

Fields of Study

  • Engineering
  • Physics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.
  • Control Systems Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference