The Use of GPR and Microwave Tomography for the Assessment of the Internal Structure of Hollow Trees

Peer reviewedPostprin

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)

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

Health monitoring of trees and investigation of tree root systems using ground penetrating radar (GPR)

Evidence suggests that trees and forests around the world are constantly being threatened by disease and environmental pressures. Over the last decade, new pathogens spread rapidly in European forests, and quarantine measures have mostly been unable to contain outbreaks. As a result, millions of trees were infected, and many of these have already died. It is therefore vital to identify infected trees in order to track, control and prevent disease spread. In addressing these challenges, the available methods often include cutting of branches and trees or incremental coring of trees. However, not only do the tree itself and its surrounding environment suffer from these methods, but they also are costly, laborious and time-consuming. In recent years the application of non-invasive testing techniques has been accepted and valued in this particular area. Given its flexibility, rapidity of data collection and cost-efficiency, Ground Penetrating Radar (GPR) has been increasingly used in this specific area of research. Consequently, this PhD Thesis aims at addressing a major challenge within the context of early identification of tree decay and tree disease control using GPR. In more detail, two main topics are addressed, namely the characterisation of the internal structure of tree trunks, and the assessment of tree root systems’ architecture. As a result, a comprehensive methodology for the assessment of both tree trunks and roots using GPR is presented, which includes the implementation of novel algorithms and GPR signal processing approaches for the characterisation of tree trunks’ internal structure and the three-dimensional mapping of tree root systems. Results of this research project were promising and will contribute towards the establishment of novel tree evaluation approaches