UWB Sensing for UAV and Human Comparative Movement Characterization

Nowadays, unmanned aerial vehicles/drones are involved in a continuously growing number of security incidents. Therefore, the research interest in drone versus human movement detection and characterization is justified by the fact that such devices represent a potential threat for indoor/office intrusion, while normally, a human presence is allowed after passing several security points. Our paper comparatively characterizes the movement of a drone and a human in an indoor environment. The movement map was obtained using advanced signal processing methods such as wavelet transform and the phase diagram concept, and applied to the signal acquired from UWB sensors

Characterization of digital modulations using the phase diagram analysis

International audienceDigital modulation identification is a challenging and important operation in the security communication field. Identifying properly a digital modulation and its parameters is a key operation in many applications, such as cognitive radio, communication intelligence and dynamic spectrum allocation. However, traditional methods face a stalemate in which the accuracy of the identification process is quite low. Therefore, new descriptors are needed as an alternative solution or to complement the existing ones. Using a new method from the field of nonlinear dynamic systems, namely the phase diagram representation, we want to extract some features to help recognizing the type of used modulations. This method can be used even in noisy backgrounds, with the help of an additional processing algorithm, to give important information about the signal and the used modulation

Low Complexity Acoustic Imaging System Based on Time of Arrivals Dynamic Estimation

International audienceThis paper addresses the problem of underwater imaging using a low complexity acoustic imagery system for various applications (i.e., immerged object localization, underwater surveillance, ocean sciences, etc.). When it comes to design a typical acoustic imaging system using multi element arrays aimed to provide high resolution images, the main principles involved are the interferometry or adaptive beamforming both of them based on phase measurements. The platform caring the acoustic imaging system must be perfectly stabilized in order to ensure an accurate imaging process. The stabilization issues are very important and current techniques employs sophisticated system, based on gyroscopes, inertial devices, etc. In this paper a simple imaging method is proposed based on acoustic sensors and the accurate estimation of the time of arrivals of the waves reflected by different scattering points of the object

On the potential of phase diagram analysis to identify the wide band modulations

International audienceThe key element of the communication signals is the information carried out by the amplitude, frequency and /or phase changes. Therefore, detecting the signal's parameters is one of the main factors that helps us to have access to the information transmitted through it. Using the phase diagram representation of a signal, a concept characteristic to the field of nonlinear dynamic systems, we want to estimate the signal's parameters and highlight some features of the modulation techniques which were used. The great advantage of this approach is the capability of phase diagram representation to present the signal's phase continuity, very useful for an accurate tracking of the communication parameters, an important application field being the communication intelligence (COMINT), when the recognition of communication signals is one of the main objectives

A novel machine learning approach in Image Pattern Recognition under invariance constraints

International audienceTwo of the main challenges of image recognition in radar, acoustic or Tray imaging regard the viewpoint variation of the pattern and the feature extraction techniques that must retrieve the most discriminative information about different classes. In this paper, we focus on feature extraction and image classification techniques by using a Rotation Invariant Wavelet Packet Decomposition and a novel entropybased feature extraction technique to characterize an image. The entropy-based characterization described in the paper offers an extended analysis compared to usual approaches such as the energy of the wavelet sub bands. The computed features will be further used to train a Graph Neural Network adapted to a quad-tree decomposition which has the powerful advantage of considering the structural information of the rotationinvariant decomposition. We successfully classified the images with an accuracy of 99.3%. The results are compared to other classic feature extraction techniques such as k-NN, SVM and WPD, proving the increased capability of our method