Fully digital, urban networked staring radar: Simulation and experimentation

Author:

Griffiths Darren1ORCID,Jahangir Mohammed2ORCID,Kannanthara Jithin1ORCID,Donlan Gwynfor1,Baker Chris J.2,Antoniou Michail2,Singh Yeshpal1

Affiliation:

1. School of Physics and Astronomy University of Birmingham Birmingham UK

2. School of Electronic Electrical and Systems Engineering University of Birmingham Birmingham UK

Abstract

AbstractThe application and uses of drones in all areas are continuously rising, especially in civilian use cases. This increasing threat requires reliable drone surveillance in urban environments. Radar is the obvious candidate with its ability to detect small objects at range, in all weather conditions. The use of an L‐band networked radar for urban sensing of S‐UAS targets is explored. Small echoes from S‐UAS places a premium on synchronisation, which is the fundamental key for high performance networked radar. The effect of timing errors on the operation of the network radar is investigated theoretically and experimentally, and the processing tools for synchronising data based on the direct signal returns of the transmitter are developed. Also, drone detection using bistatic L‐band staring radar is achieved both in simulation and then in real field trials where the SNR and detection performance are computed and analysed. The updated direct signal synchronisation method for bistatic staring radar is shown to provide comparable SNR and positional accuracy for S‐UAS targets as the monostatic staring radar.

Funder

Defence Science and Technology Laboratory

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering

Reference55 articles.

1. A review of security threats of unmanned aerial vehicles and mitigation steps;Sathyamoorthy D.;The Journal of Defence and Security,2015

2. Security analysis of drones systems: Attacks, limitations, and recommendations

3. Managing the drone revolution: A systematic literature review into the current use of airborne drones and future strategic directions for their effective control

4. Drone detection with x‐band ubiquitous radar;Quevedo D.A.D.;Proceedings International Radar Symposium,2018

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