Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar

Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity. In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system

The GLINT10 field trial results

Autonomous underwater vehicles (AUVs) have gained more interest in recent years for military as well as civilian applications. One potential application of AUVs is for the purpose of undersea surveillance. As research into undersea surveillance using AUVs progresses, issues arise as to how an AUV acquires, acts on, and shares information about the undersea battle space. These issues naturally touch on aspects of vehicle autonomy and underwater communications, and need to be resolved through a spiral development process that includes at sea experimentation. This paper presents a recent AUV implementation for active anti-submarine warfare tested at sea in the summer of 2010. On-board signal processing capabilities and an adaptive behavior are discussed in both a simulation and experimental context. The implications for underwater surveillance using AUVs are discussed

Multi-time Frequency Analysis and Classification of a Micro Drone Carrying Payloads using Multistatic Radar

This article presents an analysis of three multi-domain transformations applied to radar data of a micro-drone operating in an open field, with a payload (between 200 and 600 g) and without a payload. Inferring the presence of a drone attempting to transport a payload beyond its normal operating conditions is a key enabler in prospective low altitude airspace security systems. Two scenarios of operation were explored, the first with the drone hovering and the second with the drone flying. Both were accomplished through real experimental trials, undertaken with the multistatic radar, NetRAD. The images generated as a result of the domain transformations were fed into a pretrained convolutional neural network (CNN), known as AlexNet and were treated as a six-class classification problem. Very promising accuracies were obtained, with on average 95.1% for the case of the drone hovering and 96.6% for the case of the drone flying. The activations that these variety of images triggered within the CNN were then visualised to better understand the specific features that the network was learning and distinguishing between, in order to successfully achieve classification

Indoor target tracking using high doppler resolution passive Wi-Fi radar

This paper describes two Doppler only indoor passive Wi-Fi tracking methods based on high Doppler resolution passive radar. Two filters are investigated in this paper, the extended Kalman filter and the sequential importance resampling (SIR) particle filter. Experimental results for these two tracking filters are presented using results from software defined passive Wi-Fi radar using a standard 802.11 access point as an illuminator. The experimental results show that the SIR particle filter performs well using Wi-Fi signals for indoor tracking with a high degree of accuracy. Proposals for simplifying the SIR particle and application to multiple target tracking are also discussed

Doctor of Philosophy

dissertationDevice-free localization (DFL) and tracking services are important components in security, emergency response, home and building automation, and assisted living applications where an action is taken based on a person's location. In this dissertation, we develop new methods and models to enable and improve DFL in a variety of radio frequency sensor network configurations. In the first contribution of this work, we develop a linear regression and line stabbing method which use a history of line crossing measurements to estimate the track of a person walking through a wireless network. Our methods provide an alternative approach to DFL in wireless networks where the number of nodes that can communicate with each other in a wireless network is limited and traditional DFL methods are ill-suited. We then present new methods that enable through-wall DFL when nodes in the network are in motion. We demonstrate that we can detect when a person crosses between ultra-wideband radios in motion based on changes in the energy contained in the first few nanoseconds of a measured channel impulse response. Through experimental testing, we show how our methods can localize a person through walls with transceivers in motion. Next, we develop new algorithms to localize boundary crossings when a person crosses between multiple nodes simultaneously. We experimentally evaluate our algorithms with received signal strength (RSS) measurements collected from a row of radio frequency (RF) nodes placed along a boundary and show that our algorithms achieve orders of magnitude better localization classification than baseline DFL methods. We then present a way to improve the models used in through-wall radio tomographic imaging with E-shaped patch antennas we develop and fabricate which remain tuned even when placed against a dielectric. Through experimentation, we demonstrate the E-shaped patch antennas lower localization error by 44% compared with omnidirectional and microstrip patch antennas. In our final contribution, we develop a new mixture model that relates a link's RSS as a function of a person's location in a wireless network. We develop new localization methods that compute the probabilities of a person occupying a location based on our mixture model. Our methods continuously recalibrate the model to achieve a low localization error even in changing environments

Multi-Sensor Data Fusion between Radio Tomographic Imaging and Noise Radar

The lack of situational awareness within an operational environment is a problem that carries high risk and expensive consequences. Radio Tomographic Imaging (RTI) and noise radar are two proven technologies capable of through-wall imaging and foliage penetration. The intent of this thesis is to provide a proof of concept for the fusion of data from RTI and noise radar. The output of this thesis will consist of a performance comparison between the two technologies followed by the derivation of a fusion technique to produce a single image. Proposals have been made for the integration of multiple-input multiple-output (MIMO) radar with RTI, however, no research has been done. Data fusion between RTI and noise radar has not been explored in academia. The impact of the expected results will provide the RTI and noise radar community a proof of concept for the fusion of data from two disparate sensor technologies. RTI is a tenured field of study at Air Force Institute of Technology (AFIT), whose results can be used to produce a platform for further options to be considered for military surveillance applications. The novelty of fusing data from RTI and noise radar is achieved with the derivation of a fusion technique utilizing Tikhonov regularization. Analyzing the results of the Tikhonov influenced techniques reveals up to a 100% error decrease in target pixel location, a 75% error decrease in target centroid location, a 28% size decrease in target pixel dispersion and a 72% improvement in an ideal solution comparison. The results of the research prove that Multi-Sensor Data Fusion (MSDF) images are of greater quality than that of the images generated by the disparate sensors independently. This effectively provides the RTI and noise radar communities a proof of concept for the fusion of data from two disparate sensor technologies

The potential of LIDAR as an antisubmarine warfare sensor

Traditionally, antisubmarine warfare (ASW) has been dominated by acoustic sensors, active and passive. Ending the Cold War, the ASW forces have refocused towards a theatre of war in the littorals, and the traditional acoustic sensors do not perform very well in such an environment. The sensors are working much closer to the surface, and there is a lot more surface traffic to disturb the acoustic environment. Environmental and topographic factors also play a major role. Removing or significantly reducing the acoustic capability, one forces the ASW forces to look to other technologies and sensors to compliment or replace the acoustic ones. This is where the interest of LIDAR as an aerial ASW sensor comes into play. The aim of this thesis is to evaluate “the potential for using LIDAR technology for aerial ASW on Norwegian ASW platforms”. In addition to this main research question, the history of LIDAR has been researched, in order to find historical and existing LIDAR projects for ASW purposes. Antisubmarine warfare is a complicated business, but speed of reaction, flexibility to change operating areas quickly and efficiently, and the ability to deploy sophisticated buoys are all in the advantage to the aerial ASW platform. But as the submarines get quieter and quieter, new means of detection must be found to cover the complicated upper layers of the water column. The signal components of LIDAR and the increasing processing capability have made LIDAR technology somewhat mature, but limitations such as scattering and attenuation of light in water are severely hampering. After a decline in ASW focus after the Cold War, the Western world is finding itself in a littoral submarine threat scenario, and do not have the sensors to sufficiently meet this threat. Several LIDAR programs have been initiated and carried through, but most have been directed towards finding and neutralizing mines. Lately, a new interest of applying LIDAR-technology in the search for submarines has risen. But LIDAR itself does not seem to be able to cover the upper layers of the water column consistently enough, and other technologies might be able to compliment LIDAR in a multi-sensor solution. Synthetic Aperture Radar (SAR) and Hyperspectral Imagery seem to be the most applicable of these. A recommendation is given to military commanders to pursue a multi-sensor pod for several areas of use by Maritime Patrol Aircraft and military helicopters

Applicability and Advantages of Implementation of MIMO Techniques in Radar Systems

High-accuracy object detection using radio frequency signal has become popular field for research since last couple of years. Huge amount of research work are being done in this field now a days. Although radar systems were invented for the purpose of military, they are also used for civil service at present. MIMO communication systems becomes popular in recent years because of higher capacity, increased coverage and better voice and data quality in telecommunication systems. The overwhelming popularity of MIMO systems draws radar researchers’ attention to study the probability of implementing MIMO techniques in radar systems. This trend has been followed in this thesis. The applicability of MIMO in radar systems has been examined along with small simulations outcomes, which ends with analysis of the result and further research probability in this field. Any type of diversity is required for MIMO radar. Some of the probable diversity techniques are discussed with a signal model along with their advantages and disadvantages. This thesis starts with a brief discussion about radar principle and different types of radar systems, followed by detailed discussion on MIMO technology and their implementation on radar systems. Angular diversity i.e. beamforming is considered, in the simulation part of the thesis, to implement MIMO. Ideal propagation environment is assumed in the simulations in order to keep the focus on the beamforming mechanism itself. Approximately 10 dB signal-to-noise ratio gain is obtained in the simulations using reasonably low number of antennas. The thesis ends up with short discussion on the advantages of MIMO application in radar along with future research possibilities in this arena

Low cost passive radar through software defined radio

Passive radars utilise existing terrestrial radio signals, such as those produced by radio or television stations, to track objects within their range. This project aims to determine the suitability of low cost USB TV tuners as hardware receivers for a Software Defined Radio (SDR) based passive radar receiver. Subsequently determining its effectiveness in producing inverse synthetic aperture radar images using data collected from Digital Television signals. Since the initial identification of passive radar, Militaries the world over have been using it as a part of electronic warfare. The evolution of SDR has enabled greater access to the technologies required to implement passive radar, with the greatest limitation being the cost of the required hardware. The availability of low cost hardware was therefore investigated to determine its suitability and subsequently the availability of passive radar to a wider audience. Research was conducted into the available SDR receivers, and comparison of specifications was made against the low cost receiver used in the project. A functional hardware platform based around the Realtek RTL2832U chipset has been developed to determine its suitability as a low cost receiver verifying its ability to coherently receive radio signals for target identification. A complex ambiguity function was implemented to interpret sampled data windows, with the output of these windows to be compared to the requirements for an inverse synthetic aperture radar input, thus determining the suitability of the device. Interpretation of the received data has identified that although the hardware is capable, a real time implementation of data processing is not yet possible, impeding the ability to determine the suitability of the receiver as an inverse synthetic aperture receiver. The results of testing show that the hardware is capable of receiving and producing radar images, however due to the bandwidth of DVB-T signals , and the bandwidth limitations inherent in RTL-SDR dongles, they have proven not to be suitable for DVB-T based inverse synthetic aperture radar receivers