埃爾比特系統

Sparse arrays allow resolving multiple direction of arrivals (DoAs), by recovering a virtual covariance matrix from incomplete physical measurements. This is typically tackled via iterative optimizers, which are often sensitive to hyperparameters, require many iterations, and do not naturally account for array imperfections. In this work, we propose Deep UNfolded Covariance completion for Sparse arrays (DUNCS), which unfolds an ADMM optimizer based on the Structured Nyquist Correlation Reconstruction (SNCR) into a fixed-depth architecture. DUNCS augments each iteration with learnable parameters and is trained based on a downstream DoA estimator. This design enables rapid inference, scalability to large virtual arrays, robustness to mild array miscalibrations, and preserves the interpretability of subspace methods. We numerically show that DUNCS provides rapid and accurate DoA recovery from various sparse array configurations.

We present our ongoing work on the veriFIRE project: a collaboration between industry and academia, aimed at applying verification to increase the reliability of a real-world, safety-critical system. Specifically, we target an airborne platform for wildfire detection, which incorporates two deep neural networks. We present an end-to-end methodology for verifying consistency properties in this system. Our approach encodes application-grounded requirements into solver-compatible queries for existing neural network verifiers. We study properties of interest over critical operational scenarios: (i) monotonicity of detector confidence as target intensity increases; and (ii) bounded detector response under physically plausible blur over the sensor. We instantiate these encodings using state-of-the-art neural network verification backends and evaluate them at scale on real background samples. For the first property, all verification queries are solved in under five minutes. For the second property, verification is substantially harder, highlighting key scalability challenges for richer, higher-dimensional specifications. Overall, the results demonstrate that meaningful, domain-specific guarantees can be obtained for industrial systems.

This paper presents an optimal L2-induced control problem for systems with multiple sector-bounded nonlinearities. Sufficient boundedness conditions for the L2-induced norm are derived in terms of a specific system of linear matrix inequalities (LMIs). Based on these conditions, an optimal state feedback control problem is then formulated and solved for the considered class of nonlinear systems. A procedure to reduce the conservatism of the derived conditions is also provided. The proposed formulation, which explicitly considers multiple sector-bounded nonlinearities, is useful because it enables optimal L2-control problems for a much wider class of nonlinearities. Indeed, by invoking the universal approximation theorem, one may represent nonlinearities that do not satisfy sector-bounded conditions as a weighted sum of sector-bounded sigmoid functions. The theoretical and procedural developments are illustrated by a numerical example consisting of the state feedback optimal L2-induced control of a forced van der Pol oscillator.

Radio frequency fingerprint identification (RFFI) enables device identification and authentication by exploiting transmitter-specific hardware impairments embedded in received RF signals. Existing RFFI methods are typically designed for a specific operating regime, such as closed-set identification, open-set recognition (OSR), or discovery of new devices, and often treat these tasks as separate algorithmic components. In this paper, we propose a holistic RFFI framework that jointly supports closed-set identification, OSR, and novel class discovery (NCD) within a single expandable architecture. The proposed method formulates RFFI as vector quantization in a learned latent feature space, where registered devices are represented by labeled codewords. This representation enables closed-set identification through codeword assignment, OSR through uncertainty measures induced by the codebook geometry, and NCD by using the latent representations and open-set uncertainty of rejected samples to organize them into new device identities. We further develop a multi-stage training procedure that combines supervised representation learning, latent-space refinement, codebook initialization, and vector-quantization-aware optimization. Experimental results on LoRa and WiFi RFFI datasets demonstrate that the proposed framework achieves accurate identification of registered devices, reliable detection of unseen transmitters, and effective discovery and registration of new device classes, highlighting the benefits of coupling the different RFFI tasks.

Utilization of noise for the control of a class of non-linear systems is presented. The application of state-multiplicative noise as a mean of control is far more limited then the use of standard determinis?tic gains. Nevertheless, so called Stochastic Anti Resonance (SAR) with state-multiplicative noise based control, do arise in a variety of situations such as in engineering applications, physics modelling, bi?ology, and models of visuo-motor tasks. Linear Matrix Inequalities based conditions from recent publications are reviewed, that character?ize stochastic stability of such nonlinear systems applying SAR. While those results dealt with systems that are, apriori, modelled using sec?tor bounded nonlinearities, we demonstrate that more general systems that can be approximated as such, can be also controlled using SAR.