Importance Sampling for Objetive Funtion Estimations in Neural Detector Traing Driven by Genetic Algorithms

To train Neural Networks (NNs) in a supervised way, estimations of an objective function must be carried out. The value of this function decreases as the training progresses and so, the number of test observations necessary for an accurate estimation has to be increased. Consequently, the training computational cost is unaffordable for very low objective function value estimations, and the use of Importance Sampling (IS) techniques becomes convenient. The study of three different objective functions is considered, which implies the proposal of estimators of the objective function using IS techniques: the Mean-Square error, the Cross Entropy error and the Misclassification error criteria. The values of these functions are estimated by IS techniques, and the results are used to train NNs by the application of Genetic Algorithms. Results for a binary detection in Gaussian noise are provided. These results show the evolution of the parameters during the training and the performances of the proposed detectors in terms of error probability and Receiver Operating Characteristics curves. At the end of the study, the obtained results justify the convenience of using IS in the training

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

DVB-T-based passive radar for silent surveillance of drones

Nowadays, Unmanned Aerial Vehicles (UAVs) and drones are used as threat’s vectors that create personal and public security issues. The unpredictable and complex motion along with the small Radar Cross Section (RCS) and low velocity makes the drone detection a challenging task for any radar system. In the context outlined earlier, the security level enabled by conventional active radar systems could be augmented by the cost-effective, non-intrusive and eco-friendly Passive Radar (PR) technology. As a PR system does not have its own transmitter, this allows reduced costs, intrinsic covert operation capability and the lack of additional electromagnetic pollution. To guarantee complete and continuous coverage, PR can effectively be integrated within conventional active radars not only to extend the surveillance coverage, acting as ‘gap-filler’, but also to reduce the probability of out of service of the surveillance system. Moreover, aiming at the monitoring of airport terminal areas or harbours, where the installation of additional sensors might be limited by regulations related to public safety and risk of interference with pre-existing systems, a network of PRs could easily be deployed to provide continuous and complete coverage. The stationary nature and the isotropic characteristic of many of the employable Illuminators of Opportunity (IoO) provide a persistent illumination of the targets of interest to generate Coherent Processing Intervals (CPIs) of long integration times (Tint) on receive to counteract the limited power density offered by the emitter. This certainly applies to many ground-based transmitters for analogue or digital radio/TV broadcasting. Among them, the emitters of the Digital Video Broadcasting-Terrestrial (DVB-T) are particularly attractive for counter-drone applications. Specifically, the high radiated power of these transmitters and the excellent coverage make them suitable for the detection of these small RCS and low altitude targets. In addition, the continuous emissions and the fine range resolution of about 20 m (equivalent monostatic range resolution yielded by a signal bandwidth of approximately 8 MHz) make them potentially able to continuously detect and discriminate closely spaced targets. Aiming at the detection of the low Signal-to-Noise Ratio (SNR) targets and at widening the DVB-T-based PR coverage area, very long integration times (up to few seconds) can be exploited if the migration effects are properly compensated. It is worth noticing that the use of long integration time allows also to improve the Doppler resolution as well as to discriminate between slowly moving targets and clutter contributions, which is of particular interest in a scenario with a high density of targets. By employing an Orthogonal Frequency-Division Multiplexing (OFDM) modulation, DVB-T signals are noise-like waveforms; thus, they provide ambiguity function with attractive properties that are nearly independent of the signal content and almost time-invariant. Eventually, since a DVB-T transmitter typically broadcasts multiple channels at different carrier frequencies, this provides the desired diversity of information that could be successfully exploited for both target detection and its localization. Recently, different authors have investigated the use of such sensor for counter-drone operations proving the capability of such technology to detect and localize small and medium drones up to a few kilometres from the PR receiver. Moreover, the capability of such sensor in simultaneous detection of drones flying near the airport area along with the conventional civil air traffic at farther ranges has been proved. This chapter reports the latest results of DVB-T-based PR for counter-drone operations obtained by the research groups of the University of Alcala´ and Sapienza University of Rome

ON THE CODING GAIN OF DYNAMIC HUFFMAN CODING APPLIED TO A WAVELET-BASED PERCEPTUAL AUDIO CODER

This paper evaluates the coding gain of using a dynamic Huffman entropy coder in an audio coder that uses a wavelet-packet decomposition that is close to the subband decomposition made by the human ear. The subband audio signals are modeled as samples of a stationary random process with laplacian probability density function because experimental results indicate that the highest coding efficiency is obtained in that case. We have also studied how the entropy coding gain varies with the band index. The proposed adaptive Huffman coding method gives rise to an average coding gain of approximately 0.25 bits per sample compared to binary coding. A further coding gain can be achieved if timevarying filter banks are used. Experimental results tell us that using a suitable method to translate the psychoacoustic information to the wavelet domain, combined with our adaptive Huffman coding scheme, binary rates of about 64 kbps can be obtained for transparent coding of CD quality monophonic audio signals. 1

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