Microwave Tomography With LSTM-Based Processing of the Scattered Field

The quantitative inspection of unknown targets or bodies by means of microwave tomography requires a proper modeling of the field scattered by the structures under test, which in turn depends on several factors related to the adopted antennas and measurement configuration. In this article, a multifrequency tomographic approach in nonconstant-exponent Lebesgue spaces is enhanced by a preliminary step that processes the measured scattered field with a neural network based on long short-term memory cells. In the considered cases, this approach allows dealing with measurements in three-dimensional settings obtained with non-ideal antennas and measurement points, while retaining a canonical two-dimensional formulation of the inverse problem. The adopted data-driven model is trained with a set of simulations of cylindrical targets performed with a finite-difference time domain method, considering a simplified bistatic measurement configuration as an initial case study. The inversion procedure is then validated with numerical simulations involving cylindrical and spherical structures

A Short-Range FMCW Radar-Based Approach for Multi-Target Human-Vehicle Detection

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 $alpha$-$eta$ 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)

Full-Wave Modeling of Near-Field Radar Data for Planar Layered Media Reconstruction

A new near-field radar modeling approach for wave propagation in planar layered media is presented. The radar antennas are intrinsically modeled using an equivalent set of infinitesimal electric dipoles and characteristic, frequencydependent, global reflection, and transmission coefficients. These coefficients determine through a plane wave decomposition wave propagation between the radar reference plane, point sources, and field points. The interactions between the antenna and layered medium are thereby inherently accounted for. The fields are calculated using 3-D Green’s functions. We validated the model using an ultrawideband frequency-domain radar with a transmitting and receiving Vivaldi antenna operating in the range 0.8–4 GHz. The antenna characteristic coefficients are obtained from near- and far-field measurements over a copper plane. The proposed model provides unprecedented accuracy for describing near-field radar measurements collected over a water layer, the frequency-dependent electrical properties of which were described using the Debye model. Layer thicknesses could be retrieved through full-wave inversion. The proposed approach demonstrated great promise for nondestructive testing of planar materials and digital soil mapping using ground-penetrating radar

Development of a Cost-Efficient Multi-Target Classification System Based on FMCW Radar for Security Gate Monitoring

Radar systems have a long history. Like many other great inventions, the origin of radar systems lies in warfare. Only in the last decade, radar systems have found widespread civil use in industrial measurement scenarios and automotive safety applications. Due to their resilience against harsh environments, they are used instead of or in addition to optical or ultrasonic systems. Radar sensors hold excellent capabilities to estimate distance and motion accurately, penetrate non-metallic objects, and remain unaffected by weather conditions. These capabilities make these devices extremely flexible in their applications. Electromagnetic waves centered at frequencies around 24 GHz offer high precision target measurements, compact antenna, and circuitry design, and lower atmospheric absorption than higher frequency-based systems. This thesis studies non-cooperative automatic radar multi-target detection and classification. A prototype of a radar system with a new microwave-radar-based technique for short-range detection and classification of multiple human and vehicle targets passing through a road gate is presented. It allows identifying 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 takes care of detection and classification. This approach, which can find applications in both security and infrastructure surveillance, relies upon the processing of the scattered-field data acquired by the radar. The developed method is based on an ad-hoc processing chain to accomplish the automatic target recognition task, which consists of blocks performing clutter and leakage removal with a frame subtraction technique, clustering with a DBSCAN approach, tracking algorithm based on the \u3b1-\u3b2 filter to follow the targets during traversal, features extraction, and finally classification of targets with a classification scheme based on support vector machines. The approach is validated in real experimental scenarios, showing its capabilities incorrectly detecting multiple targets belonging to different classes (i.e., pedestrians, cars, motorcycles, and trucks). The approach has been validated with experimental data acquired in different scenarios, showing good identification capabilities

Improving the GPR reflection method for estimating soil moisture and detection of capillary fringe and water table in a boreal agricultural field

The objective of this thesis was to monitor the soil moisture (SM) and water table depth (WTD) in an agricultural field using ground-penetrating radar (GPR). First, SM was estimated using hyperbola-fitting method (27-50 cm depth range) and compared with vertically installed 30 cm long Time Domain Reflectometry (TDR) probe data. TDR-measured and GPR-estimated SM were not significantly different, and the root mean square error (RMSE) was 0.03 m3 m-3. Second, the depth of the capillary fringe (DCF) was estimated distinguishing the reflections from the top of the capillary fringe in a GPR radargram. A site-specific strong linear relationship (R2 = 0.9778) of DCF and measured-WTD was developed. RMSE between GPR-based WTD and actual WTD was 0.194 m. Proposed average capillary height for the particular site throughout the growing season (0.741 m) agrees with the existing literature and would be beneficial for the agricultural water management in the region

Near field sensing and antenna design for wireless body area network

PhD ThesisWireless body area network (WBAN) has emerged in recent years as a special class of wireless sensor network; hence, WBAN inherits the wireless sensor network challenges of interference by passive objects in indoor environments. However, attaching wireless nodes to a person’s body imposes a unique challenge, presented by continuous changes in the working environment, due to the normal activities of the monitored personnel. Basic activities, like sitting on a metallic chair or standing near a metallic door, drastically change the antenna behaviour when the metallic object is within the antenna near field. Although antenna coupling with the human body has been investigated by many recent studies, the coupling of the WBAN node antenna with other objects within the surrounding environment has not been thoroughly studied. To address the problems above, the thesis investigates the state-of-the art of WBAN, eximanes the influence of metallic object near an antenna through experimental studies and proposes antenna design and their applications for near field environments. This thesis philosophy for the previously mentioned challenge is to examine and improve the WBAN interaction with its surrounding by enabling the WBAN node to detect nearby objects based solely on change in antenna measurements. The thesis studies the interference caused by passive objects on WBAN node antenna and extracts relevant features to sense the object presence within the near field, and proposes new design of WBAN antenna suitable for this purpose. The major contributions of this study can be summarised as follows. First, it observes and defines the changes in the return loss of a narrow band antenna when a metallic object is introduced in its near field. Two methods were proposed to detect the object, based on the refelction coefficient and transmission coefficient of an antenna in free space. Then, the thesis introduces a new antenna design that conforms to the WBAN requirements of size, while achieving very low sensitivity to human body. This was achieved through combining two opposite Vivaldi shapes on one PCB and using a metallic sheet to act as a reflector, which minimised the antenna coupling with the human body and reduced the radiation pattern towards the body. Finally, the proposed antennas were tested on several human body parts with nearby metallic objects, to compare the change in antenna s-parameters due to presence of the human body and presence of the metallic object. Based on the measurements, basic statistical indicators and Principal Component Analysis were proposed to detect object presense and estimate its distance. In conclusion, the thesis successfully shows WBAN antenna’s ability to detect nearby metallic objects through a set of proposed indicators and novel antenna design. The thesis is wrapped up by the suggestion to investigate time domain features and modulated signal for future work in WBAN near field sensing