Microwave imaging for security applications

Microwave imaging technologies have been widely researched in the biomedical field where they rely on the imaging of dielectric properties of tissues. Healthy and malignant tissue have different dielectric properties in the microwave frequency region, therefore, the dielectric properties of a human body’s tissues are generally different from other contraband materials. Consequently, dielectric data analysis techniques using microwave signals can be used to distinguish between different types of materials that could be hidden in the human body, such as explosives or drugs. Other concerns raised about these particular imaging systems were how to build them cost effectively, with less radiation emissions, and to overcome the disadvantages of X-ray imaging systems. The key challenge in security applications using microwave imaging is the image reconstruction methods adopted in order to gain a clear image of illuminated objects inside the human body or underneath clothing. This thesis will discuss in detail how microwave tomography scanning could overcome the challenge of imaging objects concealed in the human body, and prove the concept of imaging inside a human body using image reconstruction algorithms such as Radon transformation image reconstruction. Also, this thesis presents subspace based TR-MUSIC algorithms for point targets and extended targets. The algorithm is based on the collection of the dominant response matrix reflected by targets at the transducers in homogenous backgrounds, and uses the MUSIC function to image it. Lumerical FDTD solution is used to model the transducers and the objects to process its response matrix data in Matlab. Clear images of metal dielectric properties have been clearly detected. Security management understanding in airports is also discussed to use new scanning technologies such as microwave imaging in the future.The main contribution of this reseach is that microwave was proved to be able to image and detect illegal objects embedded or implanted inside human body

An electromagnetic imaging system for metallic object detection and classification

PhD ThesisElectromagnetic imaging currently plays a vital role in various disciplines, from engineering to medical applications and is based upon the characteristics of electromagnetic fields and their interaction with the properties of materials. The detection and characterisation of metallic objects which pose a threat to safety is of great interest in relation to public and homeland security worldwide. Inspections are conducted under the prerequisite that is divested of all metallic objects. These inspection conditions are problematic in terms of the disruption of the movement of people and produce a soft target for terrorist attack. Thus, there is a need for a new generation of detection systems and information technologies which can provide an enhanced characterisation and discrimination capabilities. This thesis proposes an automatic metallic object detection and classification system. Two related topics have been addressed: to design and implement a new metallic object detection system; and to develop an appropriate signal processing algorithm to classify the targeted signatures. The new detection system uses an array of sensors in conjunction with pulsed excitation. The contributions of this research can be summarised as follows: (1) investigating the possibility of using magneto-resistance sensors for metallic object detection; (2) evaluating the proposed system by generating a database consisting of 12 real handguns with more than 20 objects used in daily life; (3) extracted features from the system outcomes using four feature categories referring to the objects’ shape, material composition, time-frequency signal analysis and transient pulse response; and (4) applying two classification methods to classify the objects into threats and non-threats, giving a successful classification rate of more than 92% using the feature combination and classification framework of the new system. The study concludes that novel magnetic field imaging system and their signal outputs can be used to detect, identify and classify metallic objects. In comparison with conventional induction-based walk-through metal detectors, the magneto-resistance sensor array-based system shows great potential for object identification and discrimination. This novel system design and signal processing achievement may be able to produce significant improvements in automatic threat object detection and classification applications.Iraqi Cultural Attaché, Londo

The use of late time response for stand off onbody concealed weapon detection

A new system for remote detection of onbody concealed weapons such as knives and handheld guns at standoff distances presented in this thesis. The system was designed, simulated, constructed and tested in the laboratory. The detection system uses an Ultrawide Band (UWB) antenna to bombard the target with a UWB electromagnetic pulse. This incident pulse induces electrical currents in the surface of an object such as a knife, which given appropriate conditions these currents generate an electromagnetic backscatter radiation. The radiated waves are detected using another UWB antenna to obtain the Late Time Response (LTR) signature of the detected object. The LTR signature was analysed using the Continuous Wavelet Transform (CWT) in order to assess the nature and the geometry of the object. The thesis presents the work which divided into two related areas. The first involved the design, simulation, fabrication, and testing of an Ultra-wide Band (UWB) antenna with operating bandwidth of 0.25 – 3.0 GHz and specific characteristics. Simulated and measured results show that the designed antenna achieves the design objectives which are, flat gain, a VSWR of around unity and distortion less transmitted narrow pulse. The operating bandwidth was chosen to cover the fundamental Complex Natural Resonance (CNR) modes of most firearms and to give a fine enough time resolution. The second area covered by this thesis presents a new approach for extract target signature based on the Continuous Wavelet Transform (CWT) applied to the scattering response of onbody concealed weapons. A series of experiments were conducted to test the operation of the detection system which involved onbody and offbody objects such as, knives, handheld guns, and a number of metallic wires of various dimensions. Practical and simulation results were in good agreement demonstrating the success of the approach of using the CWT in analyzing the LTR signature which is used for the first time in this work. Spectral response for every target could be seen as a distribution in which the energy level and life-time depended on the target material and geometry. The spectral density provides very powerful information concerning target unique signature

High resolution source localization in near field sensor arrays by MVDR technique

Research over the last decade has led to technological advances in high frequency active and passive detection technology and signal processing. An emerging application area is the standoff detection of concealed objects such as weapons and explosives using penetrating electromagnetic radiation such as terahertz waves (THz). Here sensor arrays are employed in the near field to image the concealed objects. A new approach is investigated to improve upon methods such as Fourier inversion and sum and delay beamforming. A method based on the Minimum Variance Distortionless Response (MVDR) filter technique is developed to localize source points in the electric field coming from a subject. To pinpoint near field sources with precision, this MVDR routine calculates filter responses along a plane that has direction of arrival angle and range axes. To understand its limitations, this new method is tested for angular resolution in various directions of arrival, ranges, and SNR levels. The results show that this technique has potential to accurately detect closely spaced point sources when only a few sensors are used to collect measurements

Late time response analysis in UWB radar for concealed weapon detection : feasibility study

Remote detection of body-worn concealed weapons or explosives (CWE) is a field of ongoing research. In this Thesis the feasibility of CWE detection by using the UWB radar is explored. The CWE detection is based on the analysis of the Late Time Response (LTR) of the human which has been illuminated by the UWB signal. A specific set of LTR parameters characterizes the target signature. Therefore the existence of a CWE attached on the human body will influence the LTR characteristics and give the composite object i.e. human-CWE a different signature than the simple object i.e. human. The CWE detection methodology is verified by theoretical analysis, modelling and extensive laboratory experimentation. Investigation of the way the LTR parameters are influenced by the existence of the CWE signifies the differences of the LTR signature between the human and human-CWE. So the resolution of the differences in the LTR of a human with and without a CWE as the main objective of the research, are presented in the Thesis. The results verify that CWE detection with the use of LTR is feasible under the experimental conditions presented. Furthermore consideration of all possible detection scenarios is out of the scope of this Thesis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Multi-Resolution Subspace-Based Optimization Method for the Retrieval of 2D Perfect Electric Conductors

Perfect Electric Conductors (PECs) are imaged integrating the subspace-based optimizationmethod (SOM) within the iterative multi-scaling scheme (IMSA). Without a-priori information on the number or/and the locations of the scatterers and modelling their EM scattering interactions with a (known) probing source in terms of surface electric field integral equations, a segment-based representation of PECs is retrieved from the scattered field samples. The proposed IMSA-SOM inversion method is validated against both synthetic and experimental data by assessing the reconstruction accuracy, the robustness to the noise, and the computational efficiency with some comparisons, as well

Concealed Explosives Detection using Swept Millimetre Waves

The aim of this project is to develop a system for the stand-o detection (typically ten metres) of concealed body-worn explosives. The system must be capable of detecting a layer of explosive material hidden under clothing and distinguishing explosives from everyday objects. Millimetre wave radar is suitable for this application. Millimetre Waves are suitable because they are not signi cantly attenuated by atmospheric con- ditions and clothing textiles are practically transparent to this radiation. Detection of explosive layers from a few mm in thickness to a few cm thickness is required. A quasi optical focussing element is required to provide su cient antenna directivity to form a narrow, highly directional beam of millimetre waves, which can be directed and scanned over the person being observed. A system of antennae and focussing optics has been modelled and built using designs from nite element analysis (FEA) software. Using the developed system, represen- tative data sets have been acquired using a Vector Network Analyser (VNA) to act as transmitter and receiver, with the data saved for processing at a later time. A novel data analysis algorithm using Matlab has been developed to carry out Fourier Transforms of the data and then perform pattern matching techniques using arti cial neural networks (ANN's). New ways of aligning and sorting data have been found using cross-correlation to order the data by similar data slices and then sorting the data by amplitude to take the strongest 50% of data sets. The signi cant contribution to knowledge of this project will be a system which can be eld tested and which will detect a layer of dielectric at a stando distance, typically of ten metres, and signal processing algorithms which can recognise the di erence 17 between the response of threat and non-threat objects. This has partially been achieved by the development of focussing optics to acquire data sets which have then been aligned by cross-correlation, sorted and then used to train a pattern matching technique using neural networks. This technique has shown good results in di erentiating between a person wearing simulated explosives and a person not carrying simulated explosives. Further work for this project includes acquiring more data sets of everyday objects and training the neural network to distinguish between threat objects and non-threat objects. The operational range also needs increasing using either a larger aperture optical element or a similarly sized Cassegrain antenna. The system needs adapting for real time use with the data processing techniques developed in Matlab. The VNA is operated over a band of 14 to 40 GHz, future work includes moving to a stand-alone transmitter and receiver operating at w-band (75 to 110 GHz)