This chapter illustrates innovative signal processing techniques and architectures for radar systems based on the exploitation of opportunistic communication signals, namely WiFi and DVB-T OFDM signals. It covers three different applications and operational scenarios where these systems can be effectively employed for surveillance purposes, namely (i) short range monitoring based on WLAN transmissions, (ii) radar systems on board of moving platforms, and (iii) low-cost forward scatter radar sensors for distributed surveillance of designed areas. In all cases, the peculiar characteristics of the considered application are discussed, and ad hoc solutions are proposed. While the proposed approaches can be exploited in both active and passive radar systems, the discussion is supported by experimental results obtained using passive radar prototypes either developed at Sapienza University of Rome or by research partners. This gives the opportunity to understand the benefits of these approaches in real world scenarios and to demonstrate their effectiveness against experimental datasets.

OFDM Radar Based on Communications Signals of Opportunity

Bongioanni, Carlo;
2024-01-01

Abstract

This chapter illustrates innovative signal processing techniques and architectures for radar systems based on the exploitation of opportunistic communication signals, namely WiFi and DVB-T OFDM signals. It covers three different applications and operational scenarios where these systems can be effectively employed for surveillance purposes, namely (i) short range monitoring based on WLAN transmissions, (ii) radar systems on board of moving platforms, and (iii) low-cost forward scatter radar sensors for distributed surveillance of designed areas. In all cases, the peculiar characteristics of the considered application are discussed, and ad hoc solutions are proposed. While the proposed approaches can be exploited in both active and passive radar systems, the discussion is supported by experimental results obtained using passive radar prototypes either developed at Sapienza University of Rome or by research partners. This gives the opportunity to understand the benefits of these approaches in real world scenarios and to demonstrate their effectiveness against experimental datasets.
2024
9788894982862
File in questo prodotto:
File Dimensione Formato  
CNIT_chapter_Sapienza_new_template.pdf

non disponibili

Tipologia: Documento in Pre-print
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 4.92 MB
Formato Adobe PDF
4.92 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14252/1868
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
social impact