ETD system

Electronic theses and dissertations repository


Tesi etd-04072015-141156

Thesis type
Tesi di dottorato di ricerca
Implementation of tracking algorithms for multistatic systems
Settore scientifico disciplinare
Corso di studi
tutor Prof. Berizzi, Fabrizio
Parole chiave
  • Multisensor
  • Multitarget
  • Tracking
Data inizio appello
Riassunto analitico
Due to the increased prevalence of ubiquitous communication technologies and the reduced cost of electronic components, there is an increasing interest in developing networked radar systems. Such networked radar systems offer potential benefits in robustness as well as improvements in performance for detection, tracking and classification.<br>As a branch of applied computer sciences sensor data fusion addresses the ability to process this vast quantity of information, generated by multiple sources, in an effective way.<br>The purpose of this thesis is to validate the tracking algorithms implemented, to determine whether they are capable of identifying and tracking two closely spaced targets, to determine the capability of the system to track a target that moves with fast maneuvers as well as the ability to handle a potential simultaneous attack from both the air and the sea. We present a method for multiple target tracking using multiple sensors both for passive and active sensors.<br>Firstly, regarding active radar, we describe an algorithm for combining range-Doppler data from multiple sensors to perform multi-target tracking. In particular we considered the problem of very poor azimuth resolution. In this case more than two sensors are needed to triangulate target tracks and techniques like multilateration are needed to<br>overcome the problem.<br>Then two tracking algorithms for bistatic DVB-T passive radar based on the Extended Kalman Filter (for single target tracking) and on the Kalman filter (for multiple target tracking), exploiting measurement of bistatic range and bistatic velocity of a target are described. Also the direction of arrival of the target is estimated through beamforming and then used in the tracking model. The algorithms have been tested and validated by using real data.