Day: 17 April 2025

Radar and Localization (C2)

Simona Ranieri 2nd year, 6 CFU, a.a. 2025-26, b-choice2B, Block C2, II semester, Subjects , ,
Radar and Localization (C2)
2 YEAR II semester 6 CFU
Prof. Mauro Leonardi A.Y. 2025-26
 

 

 (By ICT)
Code: 80300159
SSD: ING-INF/03

Objectives: Knowledge of the main applications and operations of radar systems with the necessary basic elements, both theoretical and technical-operational. Being aware, at the system level, performance in terms of scope, discrimination, ambiguity, Doppler filtering (MTI Improvement Factor) and Pulse Compression (analysis of waveform radar). Course content General information on radar, spectrum usage, radar measurements (distance, radial velocity, angular location). Fundamental radar equation, the noise of the receiver and antenna, propagation (attenuation and reflections), losses. Radar Cross Section and targets models (slow and rapid fluctuation); detection of targets (fixed and moving); integration of the pulses. Decision Theory and radar detection: decision criteria, detection of single pulse, revelation with N pulses. Doppler radar and Moving Target Indicator (MTI): Doppler effect and structure of the coherent transceiver, MTI filtering, Improvement factor and its limitations, Moving Target Detector, Matched filter and Pulse Compression, Chirp signal, ambiguity function. Global Navigation Satellite Systems principles and Mobile Terminal Localization.

MACHINE LEARNING METHODS FOR PHYSICS (D)

Simona Ranieri 2nd year, 8 CFU, Block D, II semester, Subjects , ,
Mathematical-Methods
2 YEAR (Block D)
 2 semester 8 CFU
(from Physics LM-17 )
Prof. Michele BUZZICOTTI A.Y. 2025-26
Code: 80300140
SSD: FIS/01
https://www.master-mass.eu/

 

  • PREREQUISITES: Basic concepts of Linear Algebra, Mathematical Analysis and Python Programming
  • OBJECTIVE: The lectures are thought to give a solid knowledge of the theoretical Machine Learning (ML) background. A special focus is given to the ML application for data analysis of physical systems. The students will also learn how to implement a typical ML model using the standard libraries in a Python environment.