Accession Number:

ADA202696

Title:

Multiple Model Adaptive Tracking of Airborne Targets.

Descriptive Note:

Master's thesis,

Corporate Author:

AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL OF ENGINEERING

Personal Author(s):

Report Date:

1988-12-01

Pagination or Media Count:

375.0

Abstract:

Over the past ten years considerable work has been accomplished at the Air Force Institute of Technology AFIT towards improving the ability of tracking airborne targets. Motivated by the performance advantages in using established models of tracking environment variables within a Kalman filter, an advanced tracking algorithm has been developed based on adaptive estimation filter structures. A multiple model bank of filters that have been designed for various target dynamics, which each accounting for atmospheric disturbance of the Forward Looking Infrared FLIR sensor data and mechanical vibrations of the sensor platform, outperforms a correlator tracker. The bank of filters provide the estimation capability to guide the pointing mechanisms of a shared aperture lasersensor system. The data is provided to the tracking algorithm via an 8 x 8-pixel tracking Field of View FOV from the FLIR image plane. Data at each sample period is compared by an enhanced correlator to a target template. These offsets are measurements to a bank of linear Kalman filters which provide estimates of the targets location in azimuth and elevation coordinates based on a Gauss-Markov acceleration model, and a reduced form of the atmospheric jitter model for the disturbance in the IR wavefront carrying future measurements. Theses. RH

Subject Categories:

  • Infrared Detection and Detectors
  • Target Direction, Range and Position Finding

Distribution Statement:

APPROVED FOR PUBLIC RELEASE