Distributed physical sensors network for the protection of critical infrastractures against physical attacks

The SCOUT project is based on the use of multiple innovative and low impact technologies for the protection of space control ground stations and the satellite links against physical and cyber-attacks, and for intelligent reconfiguration of the ground station network (including the ground node of the satellite link) in the case that one or more nodes fail. The SCOUT sub-system devoted to physical attacks protection, SENSNET, is presented. It is designed as a network of sensor networks that combines DAB and DVB-T based passive radar, noise radar, Ku-band radar, infrared cameras, and RFID technologies. The problem of data link architecture is addressed and the proposed solution described

Distributed physical sensors network for the protection of critical infrastractures against physical attacks

The SCOUT project is based on the use of multiple innovative and low impact technologies for the protection of space control ground stations and the satellite links against physical and cyber-attacks, and for intelligent reconfiguration of the ground station network (including the ground node of the satellite link) in the case that one or more nodes fail. The SCOUT sub-system devoted to physical attacks protection, SENSNET, is presented. It is designed as a network of sensor networks that combines DAB and DVB-T based passive radar, noise radar, Ku-band radar, infrared cameras, and RFID technologies. The problem of data link architecture is addressed and the proposed solution described

Artificial Neural Network-Based Clutter Reduction Systems for Ship Size Estimation in Maritime Radars

The existence of clutter in maritime radars deteriorates the estimation of some physical parameters of the objects detected over the sea surface. For that reason, maritime radars should incorporate efficient clutter reduction techniques. Due to the intrinsic nonlinear dynamic of sea clutter, nonlinear signal processing is needed, what can be achieved by artificial neural networks (ANNs). In this paper, an estimation of the ship size using an ANN-based clutter reduction system followed by a fixed threshold is proposed. High clutter reduction rates are achieved using 1-dimensional (horizontal or vertical) integration modes, although inaccurate ship width estimations are achieved. These estimations are improved using a 2-dimensional (rhombus) integration mode. The proposed system is compared with a CA-CFAR system, denoting a great performance improvement and a great robustness against changes in sea clutter conditions and ship parameters, independently of the direction of movement of the ocean waves and ships