Repository logo
  • English
  • العربية
  • বাংলা
  • Català
  • Čeština
  • Deutsch
  • Ελληνικά
  • Español
  • Suomi
  • Français
  • Gàidhlig
  • हिंदी
  • Magyar
  • Italiano
  • Қазақ
  • Latviešu
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Српски
  • Svenska
  • Türkçe
  • Yкраї́нська
  • Tiếng Việt
Log In
New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Scholalry Output
  3. Publications
  4. Air-to-Air Tracking of a Maneuvering Target with Gimbaled Radar
 
  • Details

Air-to-Air Tracking of a Maneuvering Target with Gimbaled Radar

Source
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS
ISSN
0731-5090
Date Issued
2016-02-01
Author(s)
Saini, Vinod
Hablani, Hari B.
DOI
10.2514/1.G001184
Volume
39
Issue
2
Abstract
This paper is concerned with the steering of an antenna beam for an air-to-air tracking system where an airborne radar antenna senses an airborne maneuvering target. A mechanical gimbal-mounted airborne radar is used to steer the antenna beam. The difficulty with a gimbaled radar is that the radar measurement becomes inaccurate if the target is outside the antenna beam width, which is usually less than 2deg. An extended Kalman filter is developed to estimate the relative position, relative velocity, and absolute acceleration of the target in Cartesian coordinates, which are further used to drive the antenna beam. Realistic noise models of sensors are incorporated in the measurements. A high-fidelity model is developed to accommodate the sensors operating at different frequencies. The mathematical formulation of initializing the state vector and process noise matrix using measurements, the measurement sensitivity matrix, and the process noise covariance matrix are presented. The flight dynamics of two fighter aircraft, both executing 3g, maneuvers is simulated to validate the proposed model. The accuracy achieved in the line-of-sight rate and line-of-sight angle estimation are deemed to be adequate for the tracking scenario. Furthermore, a MonteCarlo analysis of 50 runs is provided to validate the extended Kalman filter.
Unpaywall
Sherpa Url
https://v2.sherpa.ac.uk/id/publication/9880
URI
https://d8.irins.org/handle/IITG2025/19491
Subjects
Engineering
Instruments & Instrumentation
IITGN Knowledge Repository Developed and Managed by Library

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Privacy policy
  • End User Agreement
  • Send Feedback
Repository logo COAR Notify