Drones are well-known threats both in military and civil environments. Identifying them accurately and localizing their trajectory is an issue that more and more methods are trying to solve. Several modalities can be used to make it such as radar, optics, radio-frequency communications and acoustics. Nevertheless radar suffers from a lack of reflected signal for small targets, optical techniques can be very difficult to set in natural environments with small targets, and self-flying drones can avoid radio detection. Consequently, this paper deals with the remaining acoustic modality and aims to localize an acoustic source, then to identify it as a drone or a noise using array measurements and a supervised learning method. The acoustic array allows to determine the source direction of arrival and a spatial filtering is performed to improve the signal to noise ratio. A focused signal is then obtained and used for characterizing the source. The performances obtained to identify this source as a drone or not are compared for two different learning models. The first one uses two classes drone and noise with a classic Support Vector Machine model while the second one is based on an One Class Support Vector Machine algorithm where only the drone class is learned. A database is generated with 7001 observations of drone flights and 3818 observations of noise recordings within a controlled environment where signals are played one at a time, given that an observation is a sequence of 0.2 s of signal. Results of localization show an average error concerning the elevation angle bounded to 3.7° whereas identification results on this database give 99.5 % and 95.6 % accuracies for the two classes approach and the one class approach, respectively. It is shown that this high accuracy is reached thanks to the intrinsic separability of the created data obtained by the different features that have been chosen to compute.
Spectral computed tomography (CT) exploits the measurements obtained by a photon counting detector to reconstruct the chemical composition of an object. In particular, spectral CT has shown a very good ability to image K-edge contrast agent. Spectral CT is an inverse problem that can be addressed solving two subproblems, namely the basis material decomposition (BMD) problem and the tomographic reconstruction problem. In this work, we focus on the BMD problem, which is ill-posed and nonlinear. The BDM problem is classically either linearized, which enables reconstruction based on compressed sensing methods, or nonlinearly solved with no explicit regularization scheme. In a previous communication, we proposed a nonlinear regularized Gauss-Newton (GN) algorithm.1 However, this algorithm can only be applied to convex regularization functionals. In particular, the ℓp (p < 1) norm or the `0 quasi-norm, which are known to provider sparse solutions, cannot be considered. In order to better promote the sparsity of contrast agent images, we propose a nonlinear reconstruction framework that can handle nonconvex regularization terms. In particular, the ℓ1/ℓ2 norm ratio is considered.2 The problem is solved iteratively using the block variable metric forward-backward (BVMF-B) algorithm,3 which can also enforce the positivity of the material images. The proposed method is validated on numerical data simulated in a thorax phantom made of soft tissue, bone and gadolinium, which is scanned with a 90-kV x-ray tube and a 3-bin photon counting detector.