In this article, a new microwave-radar-based technique for short-range detection and classification of multiple human and vehicle targets crossing a monitored area is proposed. This approach, which can find applications in both security and infrastructure surveillance, relies upon the processing of the scattered-field data acquired by low-cost off-the-shelf components, i.e., a 24 GHz frequency-modulated continuous wave (FMCW) radar module and a Raspberry Pi mini-PC. The developed method is based on an ad hoc processing chain to accomplish the automatic target recognition (ATR) task, which consists of blocks performing clutter and leakage removal with an infinite impulse response (IIR) filter, clustering with a density-based spatial clustering of applications with noise (DBSCAN) approach, tracking using a Benedict-Bordner $\alpha $ - $\beta $ filter, features extraction, and finally classification of targets by means of a $k$ -nearest neighbor ( $k$ -NN) algorithm. The approach is validated in real experimental scenarios, showing its capabilities in correctly detecting multiple targets belonging to different classes (i.e., pedestrians, cars, motorcycles, and trucks).

A phaseless tomographic method for microwave imaging is presented in this paper. The developed algorithm aims at retrieving the distributions of the dielectric properties of a predefined region under test, starting from amplitude-only electric field measurements. The use of this kind of input data allows cheaper measurement setups that do not require a coherent detection or complex phase synchronization approaches. Inside the imaging procedure, the non-linear relationship between the amplitude-only measured data and the dielectric properties of the targets is inverted by using an iterative Newton scheme. The core of this approach is represented by a Lebesgue-space regularization algorithm, which is adopted for computing the solution update at each linearization step. The capabilities of the proposed imaging method are evaluated in numerically simulated scenarios, as well as by using experimental field measurements.

A prototype of a radar system for classification of multiple targets passing through a road gate is presented in this paper. It allows to identify different types of targets, i.e., pedestrians, motorcycles, cars, and trucks. The developed system is based on a low-cost 24 GHz off-the-shelf FMCW radar, combined with an embedded Raspberry Pi PC for data acquisition and transmission to a remote processing PC, which take care of detection and classification. The processing chain relies upon a tracking algorithm to follow the targets during traversal, combined with a classification scheme based on support vector machines. The approach has been validated with experimental data acquired in different scenarios, showing good identification capabilities.

A systematic survey of the state-of-the-art on the systems merging radio frequency identification (RFID) and computer vision (CV) is reported in this article. This review is structured on the basis of the main application contexts in which these systems have been proposed: 1) inventory; 2) augmented reality (AR); 3) perception of human activity and human computer interaction (HCI); 4) robotics; 5) tracking of generic tagged targets; 6) assistance to elderly adults or people with disabilities; 7) electronic article surveillance (EAS); and 8) medical and veterinary research. The presented survey aims to summarize the algorithmic features of the existing approaches, and highlight the solved challenges and the limits of the available solutions.

Correct regulation of meteoric surface and subsurface flow waters is a fundamental goal for the sustainable development of the territories. A new system, aimed at real-time monitoring of the rainfall and of the cumulated rainfall, is introduced and discussed in the present paper. The system implements a Sensor Network based on the IoT paradigm and can cover safety-critical “hot spots” with a relatively small number of sensors, strategically placed, in areas not covered by traditional weather radars and rain gauges, and lowering the costs of deployment and maintenance with respects to these devices. A real application case, based on the implementation of the pilot plant at the Monte Scarpino landfill (Genoa, Italy), is presented and discussed. The system performances are assessed on the basis of comparisons with data provided by a polarimetric weather radar and by a traditional rain gauge.

In this paper, a microwave imaging method able to inspect three-dimensional configurations is proposed. The approach is developed in Lp Banach spaces, and allows to suitably face the ill-posedness of the considered full vector inverse scattering problem. The assumed nonconventional spaces require several modifications in the original inexact-Newton scheme, which give rise to a significant improvement in reducing the over-smoothing effects in the reconstructed distributions of the dielectric properties of the targets. The proposed approach is discussed in details and validated by using both numerical simulations and real data.