Design and optimization of a high resolution radar data processing system

Corredor Salinas, Diego Leonardo and Piñeros Prada, Carlos Andrés (2015). Design and optimization of a high resolution radar data processing system. Thesis (Master thesis), E.T.S.I. Telecomunicación (UPM).

Description

Title: Design and optimization of a high resolution radar data processing system
Author/s:
  • Corredor Salinas, Diego Leonardo
  • Piñeros Prada, Carlos Andrés
Contributor/s:
  • Burgos García, Mateo
Item Type: Thesis (Master thesis)
Masters title: Radar. Tecnologías, Equipos y Desarrollo de Sistemas
Date: March 2015
Subjects:
Freetext Keywords: Blobs, Clustering, Coordinate System, Gating, High-Resolution Radar, Kalman Filter, Monte-Carlo, Multi-Target Tracking, Nearest Neighbor, Radar Detection Matrix, Singer dynamic Model, Tracks, Track-While-Scan, α-β Filter
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - Share

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Abstract

An implementation of a complete High-Resolution Radar Data Processing System is performed, including Clustering, Gating, measurement-to-track association and track filtering algorithms. For the last mentioned, various schemes are implemented, such as a classic α-β Filter and a debiasing compensation Kalman Filter. A useful Detection Matrix Simulation tool is developed to test the overall system performance. In addition, the system is tested using real radar data. Subsequently, a method to initialize Kalman Filter in Cartesian Coordinates, using a α-β Filter in Polar Coordinates is proposed, proving to be useful when there are targets in the Radar FOV with initial acceleration or that have a high measurement error due to their distance to the Radar. Finally, based on results obtained in tests, parameter adjustment is perform to optimize the system performance.

More information

Item ID: 49787
DC Identifier: http://oa.upm.es/49787/
OAI Identifier: oai:oa.upm.es:49787
Deposited by: Diego Corredor
Deposited on: 20 Mar 2018 13:54
Last Modified: 20 Sep 2018 22:30
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