Deep neural networks (DNNs) have become a relevant subject in the classification of radio frequency signals and remote sensing data. A primary challenge is a tradeoff between obtaining data that are suitable for DNN training and the effort that making experimental measurements requires. Hence, the quality and quantity of data used for the training and testing of models are crucial for effective classifier development. The training dataset should cover a wide range of cases that synthesize the actual scenarios being classified. This work proposes a novel data augmentation method based on a deterministic model to generate a simulated dataset of radar micro-Doppler signatures suitable for unmanned aerial vehicle (UAV) target classification, without requiring measurement data. It is shown that the DNN trained using the properly generated model-based data offers improved classification accuracy performance. Results are presented for a two-class classification of the number of UAV motors using a 77-GHz frequency-modulated continuous-wave (FMCW) automotive radar system. The effectiveness of the proposed methodology is proven: a classification accuracy of 78.68% is achieved using a convolutional neural network (CNN) trained using the synthetic dataset, while an accuracy of 66.18% is achieved by using a typical signal processing data augmentation method on a limited measured dataset.

With the increase in small unmanned aerial vehicle (UAV) applications in several technology areas, detection and small UAVs classification have become of interest. To cope with small radar cross-sections (RCSs), slow-flying speeds, and low flying altitudes, the micro-Doppler signature provides some of the most distinctive information to identify and classify targets in many radar systems. In this paper, we introduce an effective model for the micro-Doppler effect that is suitable for frequency-modulated continuous-wave (FMCW) radar applications, and exploit it to investigate UAV signatures. The latter depends on the number of UAV motors, which are considered vibrational sources, and their rotation speed. To demonstrate the reliability of the proposed model, it is used to build simulated FMCW radar images, which are compared with experimental data acquired by a 77 GHz FMCW multiple-input multiple-output (MIMO) cost-effective automotive radar platform. The experimental results confirm the model’s ability to estimate the class of the UAV, namely its number of motors, in different operative scenarios. In addition, the experimental results show that the motors rotation speed does not imprint a significant signature on the classification of the UAV; thus, the estimation of the number of motors represents the only viable parameter for small UAV classification using the micro-Doppler effect.

This paper describes a distributed point-source monitoring platform for gas level and leakage detection in hazardous environments. The platform, based on a wireless sensor network (WSN) architecture, is organised into sub-networks to be positioned in the plant’s critical areas; each sub-net includes a gateway unit wirelessly connected to the WSN nodes, hence providing an easily deployable, stand-alone infrastructure featuring a high degree of scalability and reconfigurability. Furthermore, the system provides automated calibration routines which can be accomplished by non-specialized maintenance operators without system reliability reduction issues. Internet connectivity is provided via TCP/IP over GPRS (Internet standard protocols over mobile networks) gateways at a one-minute sampling rate. Environmental and process data are forwarded to a remote server and made available to authenticated users through a user interface that provides data rendering in various formats and multi-sensor data fusion. The platform is able to provide real-time plant management with an effective; accurate tool for immediate warning in case of critical events.

In this letter, we present an anchor node suitable for 2-D direction-of-arrival estimation in indoor environments. It is enabled by the space and frequency division multiple access concept, and it is exploited in a dual-band IEEE 802.11 network operating at both 2.45 and 5.2 GHz. The anchor is suited for position estimators that compare the real-time system received signal strength indicator with the expected signal levels, from any possible position and without any prior calibration stage, to estimate the actual position. An independent experimental validation demonstrates the system performance in a 2-D scenario with respect to several position estimators: within an area of 25 m$^2$, a mean error of 37 cm is observed, while the 90th percentile of the cumulative error distribution is 52 cm.

A highly linear active mixer in Gallium Nitride on Silicon (GaN-on-Si) monolithic microwave integrated circuit (MMIC) technology, operating in Ka-band for radar applications is introduced. It exhibits a local oscillator (LO) balanced topology of gate-pumped mixers based on high-electron mobility transistors (HEMT) and operates in down-conversion mode. Its design principles and the theoretical foundation of its linearity features resulting from both the high power handling capability of GaN-on-Si HEMTs and the corresponding high applied LO level are presented. An extensive characterization based on excitation of a prototype by broadband continuous wave signals and frequency modulated continuous wave signals validates both the analytical treatment of its linearity and the design of the prototype of the mixer. In the 35–40 GHz operating band, this prototype exhibits a state-of-the-art input referred third order intercept point that reaches 39.1 dBm, and a maximum input referred second order intercept point of 39.4 dBm, with a corresponding conversion gain of −5.2 dB, and LO level of 24 dBm.

This paper presents a microstrip array antenna designed for a $2\times 2$ Compressive Sensing Multiple-Input Multiple-Output (CS-MIMO) millimeter-wave radar operating at 37.5 GHz. The CS-MIMO linear array antenna is designed to obtain an optimal aperture by seeking a suitable random pattern for the antenna positions. Applying CS allows a considerable reduction in the number of antennas respect to a dense array based on the Nyquist criterion. In this study, we report all possible configurations of $2\times 2$ CS-MIMO by placing antennas in random positions, plus their compression ratio. Finally, by selecting the proper design, we examine the experimental validation of the CS-MIMO antenna prototype by comparing measurements and simulations with a Standard MIMO (Std-MIMO) antenna prototype as a benchmark. The experimental results show that the angular resolution can be increased through a random array CS-MIMO by a factor of at least 2.9 respect to Std-MIMO while preserving the radar field of view.