This thesis presents a model to predict signal strength for frequencies between 902 MHz and 28 GHz. The model approximates diffraction using the knife-edge concept and equations proposed by Lee (1985). LOS pathways are calculated using the Bresenham algorithm and the corresponding elevations are obtained from a 30m DEM base map. The base map was generated by the procedure outlined in Rose (2001) and includes building elevations. The effect of Fresnel zones on prediction accuracy is considered. The effect of interpolating elevations along the Bresenham line is also considered. An Inverse Distance Weighting algorithm was used to interpolate the elevations. The accuracy of the model was evaluated using received signal strength data compiled from studies conducted at 902 MHz, 24.12 GHz and 27.525 GHz. In addition to the compiled data, data was also collected for this study at 2.4 GHz. 257 receiver locations were evaluated; 70 samples were Line-of-Sight. The study area incorporates the Virginia Polytechnic Institute and State University campus. Incorporating Fresnel zones, Interpolating elevations and calculating double blockages do not have an effect on the program's overall ability to predict signal strength. However, for obstructed pathways, it is not adequate to simply use path loss as an estimate of signal strength. Accurate estimates of diffraction gain are crucial for obstructed pathways. In addition, examination of the standard deviation for the data sets indicates that the model is independent of frequency. The average error across the frequencies is positively correlated with frequency, indicating that the model predicts signal strength better for higher frequencies. The smaller wavelengths associated with the higher frequencies require a more directional antenna and are therefore less sensitive to multipath interference. In addition, the smaller wavelengths are less able to diffract around buildings and terrain features. / Master of Science

The purpose of this study was to determine if light detection and ranging (LiDAR) imagery could provide a significantly more accurate data set for modeling near line-of-sight (LOS) propagation at higher frequencies, specifically 27.810 GHz. than a USGS digital elevation model (DEM). In addition, the study tested for significant differences in LiDAR elevation data created at various resolutions ranging from 1 to 100 meters. Finally, this study examined the effects of various classification thresholds for transforming continuous signal strength measurements into LOS or non-LOS (NLOS) classifications used in determining prediction accuracy. The capability to transmit information via higher frequency wireless equipment requires a near LOS path between the transmitter and the antenna receiving the signal. USGS DEMs, commonly used in GIS programs to predict communication viewsheds (commsheds), represent the bare earth topography and do not reflect surface features such as vegetation and buildings. In actuality these surface features can significantly influence near LOS paths and therefore a data set that contains these features can greatly improve the ability to predict commshed areas. LiDAR is a form of active imagery that records both the bare-earth as well as these surface features, at a high resolution, making it well suited for wireless modeling applications. Results indicate that signal strength threshold classification has a direct influence on the accuracy of predicted commsheds across all resolutions. Secondly, LiDAR resolutions lower than 40m as well as bare-earth DEMs were unsuccessful in predicting an accurate commshed while LiDAR resolutions coarser than 15m provided significant predictions of equal accuracy. These results indicate that high resolution LiDAR is needed to accurately model commsheds but signal strength threshold classification determines which of these higher resolutions are significant. / Master of Science

Over the past decade, the possibility of forming images of objects hidden from line-of-sight (LOS) view has emerged as an intriguing and potentially important expansion of computational imaging and computer vision technology. This capability could help soldiers anticipate danger in a tunnel system, autonomous vehicles avoid collision, and first responders safely traverse a building. In many scenarios where non-line-of-sight (NLOS) vision is desired, the LOS view is obstructed by a wall with a vertical edge. In this thesis we show that through modeling and computation, the impediment to LOS itself can be exploited for enhanced resolution of the hidden scene. NLOS methods may be active, where controlled illumination of the hidden scene is used, or passive, relying only on already present light sources. In both active and passive NLOS imaging, measured light returns to the sensor after multiple diffuse bounces. Each bounce scatters light in all directions, eliminating directional information. When the scene is hidden behind a wall with a vertical edge, that edge occludes light as a function of its incident azimuthal angle around the edge. Measurements acquired on the floor adjacent to the occluding edge thus contain rich azimuthal information about the hidden scene. In this thesis, we explore several edge-resolved NLOS imaging systems that exploit the occlusion provided by a vertical edge. In addition to demonstrating novel edge-resolved NLOS imaging systems with real experimental data, this thesis includes modeling, performance bound analyses, and inversion algorithms for the proposed systems. We first explore the use of a single vertical edge to form a 1D (in azimuthal angle) reconstruction of the hidden scene. Prior work demonstrated that temporal variation in a video of the floor may be used to image moving components of the hidden scene. In contrast, our algorithm reconstructs both moving and stationary hidden scenery from a single photograph, without assuming uniform floor albedo. We derive a forward model that describes the measured photograph as a nonlinear combination of the unknown floor albedo and the light from behind the wall. The inverse problem, which is the joint estimation of floor albedo and a 1D reconstruction of the hidden scene, is solved via optimization, where we introduce regularizers that help separate light variations in the measured photograph due to floor pattern and hidden scene, respectively. Next, we combine the resolving power of a vertical edge with information from the relationship between intensity and radial distance to form 2D reconstructions from a single passive photograph. We derive a new forward model, accounting for radial falloff, and propose two inversion algorithms to form 2D reconstructions from a single photograph of the penumbra. The performances of both algorithms are demonstrated on experimental data corresponding to several different hidden scene configurations. A Cramer-Rao bound analysis further demonstrates the feasibility and limitations of this 2D corner camera. Our doorway camera exploits the occlusion provided by the two vertical edges of a doorway for more robust 2D reconstruction of the hidden scene. This work provides and demonstrates a novel inversion algorithm to jointly estimate two views of change in the hidden scene, using the temporal difference between photographs acquired on the visible side of the doorway. A Cramer-Rao bound analysis is used to demonstrate the 2D resolving power of the doorway camera over other passive acquisition strategies and to motivate the novel biangular reconstruction grid. Lastly, we present the active corner camera. Most existing active NLOS methods illuminate the hidden scene using a pulsed laser directed at a relay surface and collect time-resolved measurements of returning light. The prevailing approaches are inherently limited by the need for laser scanning, a process that is generally too slow to image hidden objects in motion. Methods that avoid laser scanning track the moving parts of the hidden scene as one or two point targets. In this work, based on more complete optical response modeling yet still without multiple illumination positions, we demonstrate accurate reconstructions of objects in motion and a `map’ of the stationary scenery behind them. This new ability to count, localize, and characterize the sizes of hidden objects in motion, combined with mapping of the stationary hidden scene could greatly improve indoor situational awareness in a variety of applications.

The current focus on the Internet of Things (IoT) has led to the emergence of many network scenarios with unlimited use cases, including smart homes, smart cities, smart agriculture, and more. Unmanned aerial vehicles (UAVs), also known as drones, have become increasingly popular due to their versatility and ability to collect and transmit data through various sensors and cameras. With real-time data transmission, autonomy, and cost-effectiveness, UAVs have become valuable tools for different applications, including disaster management, agriculture monitoring, and remote area control. Low-power wide-area network (LPWAN) technology plays a crucial role in enabling IoT, and LoRaWAN is one of the specific LPWAN communication technologies that can provide low power consumption and coverage over a wide range. During a catastrophe, wireless communication is critical for analyzing damaged regions, coordinating rescue and relief team actions, saving lives, and reducing economic losses. UAVs can partially replace damaged or overloaded wireless networks as an alternative wireless network provider. This thesis aimed to simulate a LoRa network and investigate the relationship between the UAV coverage radius and elevation angle, as well as the effect of multipath distortion and signal attenuation on UAV and user distance. By calculating signal-to-noise ratio (SNR) and bit error rate (BER) for LoRa in a line-of-sight (LoS) and non-line-of-sight (NLoS) environment, we provided a comprehensive analysis of LoRaWAN performance in real-life environments for long distances. The results indicate that LoRaWAN communication is reliable in various environments, making it a promising technology for emergency and medical communications. / Det nuvarande fokuset på Internet of Things (IoT) har lett till uppkomsten av många nätverksscenarier med obegränsade användningsfall, inklusive smarta hem, smarta städer, smart jordbruk och mer. Obemannade flygfarkoster (UAV), även kända som drönare, har blivit allt populärare på grund av deras mångsidighet och förmåga att samla in och överföra data genom olika sensorer och kameror. Med realtidsdataöverföring, autonomi och kostnadseffektivitet har UAVs blivit värdefulla verktyg för olika applikationer, inklusive katastrofhantering, jordbruksövervakning och fjärrkontroll av områden. Low-power wide-area network (LPWAN)-teknik spelar en avgörande roll för att möjliggöra IoT, och LoRaWAN är en av de specifika LPWAN-kommunikationsteknikerna som kan ge låg strömförbrukning och täckning över ett brett spektrum. Under en katastrof är trådlös kommunikation avgörande för att analysera skadade regioner, koordinera räddnings- och hjälpteams åtgärder, rädda liv och minska ekonomiska förluster. UAV:er kan delvis ersätta skadade eller överbelastade trådlösa nätverk som en alternativ leverantör av trådlöst nätverk. Detta examensarbete syftade till att simulera ett LoRa-nätverk och undersöka sambandet mellan UAV-täckningsradien och höjdvinkeln, såväl som effekten av flervägsdistorsion och signaldämpning på UAV och användaravstånd. Genom att beräkna signal-brusförhållande (SNR) och bitfelsfrekvens (BER) för LoRa i en siktlinje (LoS) och icke-siktlinje (NLoS) miljö, gav vi en omfattande analys av LoRaWAN prestanda i verkliga miljöer för långa avstånd. Resultaten indikerar att LoRaWAN-kommunikation är tillförlitlig i olika miljöer, vilket gör den till en lovande teknik för akut- och medicinsk kommunikation.

Unmanned Aircraft Systems (UAS) face one common challenge when integrating with the existing manned aircraft population in the National Airspace System (NAS). To unlock the full efficiency of UAS, the UAS integrator must comply with an onboard pilot’s requirement to see-and-avoid other aircraft while operating. Commercially available Detect-and-Avoid (DAA) sensor technologies have been developed to attempt to comply with this requirement. UAS integrators must use these sensors to meet or exceed the performance of a human pilot. This thesis covers research done to integrate an array of commercially made DAA sensors with a large Group 3 UAS both in hardware and software that was later flight tested and evaluated for usability. A fast-time simulation is presented using the principles of the National Aeronautics and Space Administration's (NASA) Detect-and-AvoID Alerting Logic for Unmanned Systems (DAIDALUS). Last, open-source tools are presented to assist future integrators in validating their DAA solutions.

No / This paper investigates the effect of channel bandwidth on the accuracy of AOA estimation algorithms based on the detection of the direct path. The accurate detection of the Line of Sight (LOS) signal in a multipath environment is crucial for reliable direction finding. In this work, the estimation algorithms are applied to the LOS component in the time domain channel impulse response which is acquired by applying the inverse Fourier transform to the simulated channel transfer function in the desired bandwidth. Different channel bandwidths as well as two AOA estimation methods have been considered in the modelling to investigate the performance of the standard deviation of angle estimation error. It has been shown that increasing the bandwidth in all simulated channel scenarios improves the estimation accuracy. / Seven Technologies Group, KTP project grant No. 008734.

This dissertation proposes a new framework to Non-Field-of-view (NFOV) sound source localization and tracking in indoor environments. The approach takes advantage of sound signal information to localize target position through auditory sensors combination with other sensors within grid-based recursive estimation structure for tracking using nonlinear and non-Gaussian observations. Three approaches to NFOV target localization are investigated. These techniques estimate target positions within the Recursive Bayesian estimation (RBE) framework. The first proposed technique uses a numerical fingerprinting solution based on acoustic cues of a fixed microphone array in a complex indoor environment. The Interaural level differences (ILDs) of microphone pair from a given environment are constructed as an a priori database, and used for calculating the observation likelihood during estimation. The approach was validated in a parametrically controlled testing environment, and followed by real environment validations. The second approach takes advantage of acoustic sensors in combination with an optical sensor to assist target estimation in NFOV conditions. This hybrid of the two sensors constructs observation likelihood through sensor fusion. The third proposed model-based technique localizes the target by taking advantage of wave propagation physics: the properties of sound diffraction and reflection. This approach allows target localization without an a priori knowledge database which is required for the first two proposed techniques. To demonstrate the localization performance of the proposed approach, a series of parameterized numerical and experimental studies were conducted. The validity of the formulation and applicability to the actual environment were confirmed. / Ph. D.

Range-based localization is a widely used technique for position estimation where distances are measured to anchors, nodes with known positions, and the position is analytically estimated. It offers the benefits of providing high localization accuracy and involving simple operation over multiple deployments. Examples are the Global Positioning System (GPS) and network-based cellular handset localization. Range-based localization is promising for a range of applications, such as robot deployment in emergency scenarios or monitoring industrial processes. However, the presence of clutter in some of these environments leads to a severe degradation of the localization accuracy due to non-line-of-sight (NLOS) signal propagation. Moreover, current literature in NLOS-mitigation techniques requires that the NLOS distances constitute only a minority of the total number of distances to anchors. The key ideas proposed in the dissertation are: 1) multi-hop localization offers significant advantages over single-hop localization in NLOS-prone environments; and 2) it is possible to further reduce position errors by carefully placing intermediate nodes among the clutter to minimize multi-hop distances between the anchors and the unlocalized node. We demonstrate that shortest path distance (SPD) based multi-hop localization algorithms, namely DV-Distance and MDS-MAP, perform the best among other competing techniques in NLOS-prone settings. However, with random node placement, these algorithms require large node densities to produce high localization accuracy. To tackle this, we show that the strategic placement of a relatively small number of nodes in the clutter can offer significant benefits. We propose two algorithms for node placement: first, the Optimal Placement for DV-Distance (OPDV) focuses on obtaining the optimal positions of the nodes for a known clutter topology; and second, the Adaptive Placement for DV-Distance (APDV) offers a distributed control technique that carefully moves nodes in the monitored area to achieve localization accuracies close to those achieved by OPDV. We evaluate both algorithms via extensive simulations, as well as demonstrate the APDV algorithm on a real robotic hardware platform. We finally demonstrate how the characteristics of the clutter topology influence single-hop and multi-hop distance errors, which in turn, impact the performance of the proposed algorithms.

Les systèmes radar à synthèse d’ouverture (RSO) sont utilisés depuis de nombreuses années pour des applications militaires telles que la détection des cibles cachées. L’amélioration constante de la résolution de ces capteurs permet aujourd’hui d’accéder à un niveau de détail élevé dans la scène imagée. Cependant, l’interprétation de ces images demeure particulièrement compliquée dans le cas des milieux urbains. En effet, ces milieux particuliers sont sièges de nombreux phénomènes physiques et d’interactions multiples qui rendent la tâche de détection difficile et parfois erronée. C’est dans ce contexte que s’inscrit cette thèse. L’objectif est d’étudier la faisabilité de détection d’une cible en non visée directe du capteur à l’intérieur d’une scène simple et représentative du milieu urbain: le canyon urbain. Une étude sur la phénoménologie de propagation électromagnétique à l’intérieur des canyons urbains est menée à l’aide de mesures en environnement contrôlé à échelle réduite. Ces mesures ont permis la validation d’un outil électromagnétique commercial pour l’étude de la propagation d’une configuration à échelle réelle. Se basant sur les résultats de simulation du code électromagnétique validé, un outil maison, dédié à la prédiction des zones de détection d’une cible à l’intérieur d’un canyon urbain et à l’analyse de la signature électromagnétique correspondante, a été développé et validé. En outre, ce code contribue à l’interprétation complète de données radiométriques et interférométriques d’une scène urbaine réelle. / Synthetic Aperture Radar (SAR) systems have been used since many years for military applications such as the detection of hidden targets. With improved resolutions of these systems, high level of details can be distinguished in the corresponding images. However, some difficulties are encountered when analyzing the SAR images of urban areas. In particular, in these areas, many physical phenomena and interactions occur that make the detection of a target a challenging task. In this framework, the goal of the thesis is to investigate the feasibility of detecting Non Line Of Sight targets inside a simple and representative scene: the urban canyon. A study of the electromagnetic (EM) phenomenology of propagation inside urban canyons has been performed using indoor data at a reduced scale. These data allowed the validation of an EM commercial tool that studies the EM propagation at a real scale. Based on the results of simulation of this code, an in-house code was developed dedicated to predict the detection of a target inside an urban canyon and to analyze the corresponding EM signature. Moreover, this code contributed to a full interpretation of InSAR data of a real complex urban scene with targets.

This thesis serves as an introduction to anyone that has an interest in artificial intelligence games and has experience in programming or anyone who knows nothing of computer games but wants to learn about it. The first part will present a brief introduction to AI, then it will give an introduction to games and game programming for someone that has little knowledge about games. This part includes game programming terminology, different game genres and a little history of games. Then there is an introduction of a couple of common techniques used in game AI. The main contribution of this dissertation is in the second part where three techniques that never were properly implemented before 3D games took over the market are introduced and it is explained how they would be done if they were to live up to today’s standards and demands. These are: line of sight, image recognition and pathfinding. These three techniques are used in today’s 3D games so if a 2D game were to be released today the demands on the AI would be much higher then they were ten years ago when 2D games stagnated. The last part is an evaluation of the three discussed topics.