International Telemetering Conference Proceedings / October 26-29, 1992 / Town and Country Hotel and Convention Center, San Diego, California / An improved ionospheric delay correction model for a transionospheric electromagnetic pulse (EMP) is used for estimating the total-electron-content (TEC) profile of the path and accurate ranging of the EMP source. For a known pair of time of arrival (TOA) measurements at two frequency channels, the ionospheric TEC information is estimated using a simple numerical technique. This TEC information is then used for computing ionospheric group delay and pulse broadening effect correction to determine the free space range. The model prediction is compared with the experimental test results. The study results show that the model predictions are in good agreement with the test results.

Given 2D images, it still remains a big challenge in the field of computer vision to group the image points into logical objects (segmentation) and to determine the locations in the scene (range estimation). Despite the decades of research, a single solution is yet to be found. Through our research we have demonstrated that a possible solution is to use moving aperture lens. This lens has the effect of introducing small, repeating movements of the camera center so that objects appear to translate in the image, by an amount that depends on distance from the plane of focus. Our novel method employs optical flow techniques to an image sequence, captured using a video camera with a moving aperture lens. For a stationary scene, optical flow magnitudes and direction are directly related to the three-dimensional object distance and location from the observer. Exploiting this information, we have successfully extracted objects at different depths and estimated the locations of objects in the scene, with respect to the plane of focus. Our work therefore demonstrates an ability for passive range estimation, without emitting any energy in an environment. Other potential applications include video compression, 3D video broadcast, teleconferencing and autonomous vehicle navigation. / Master of Science

Gesturing is a natural way of communication between people and is used in our everyday conversations. Hand gesture recognition systems are used in many applications in a wide variety of fields, such as mobile phone applications, smart TVs, video gaming, etc. With the advances in human-computer interaction technology, gesture recognition is becoming an active research area. There are two types of devices to detect gestures; contact based devices and contactless devices. Using ultrasonic waves for determining gestures is one of the ways that is employed in contactless devices. Hand gesture recognition utilizing ultrasonic waves will be the focus of this thesis work. This thesis presents a new method for detecting and classifying a predefined set of hand gestures using a single ultrasonic transmitter and a single ultrasonic receiver. This method uses a linear frequency modulated ultrasonic signal. The ultrasonic signal is designed to meet the project requirements such as the update rate, the range of detection, etc. Also, it needs to overcome hardware limitations such as the limited output power, transmitter, and receiver bandwidth, etc. The method can be adapted to other hardware setups. Gestures are identified based on two main features; range estimation of the moving hand and received signal strength (RSS). These two factors are estimated using two simple methods; channel impulse response (CIR) and cross correlation (CC) of the reflected ultrasonic signal from the gesturing hand. A customized simple hardware setup was used to classify a set of hand gestures with high accuracy. The detection and classification were done using methods of low computational cost. This makes the proposed method to have a great potential for the implementation in many devices including laptops and mobile phones. The predefined set of gestures can be used for many control applications.

Due to the wide field of view, omnidirectional cameras have been extensively used in many applications, including surveillance and autonomous navigation. In order to implement a fully autonomous system, one of the essential problems is construction of an accurate, dynamic environment model. In Computer Vision this is called structure from stereo or motion (SFSM). The work in this dissertation addresses omnidirectional vision based SFSM for the navigation of an autonomous surface vehicle (ASV), and implements a vision system capable of locating stationary obstacles and detecting moving objects in real time. The environments where the ASV navigates are complex and fully of noise, system performance hence is a primary concern. In this dissertation, we thoroughly investigate the performance of range estimation for our omnidirectional vision system, regarding to different omnidirectional stereo configurations and considering kinds of noise, for instance, disturbances in calibration, stereo configuration, and image processing. The result of performance analysis is very important for our applications, which not only impacts the ASV's navigation, also guides the development of our omnidirectional stereo vision system. Another big challenge is to deal with noisy image data attained from riverine environments. In our vision system, a four-step image processing procedure is designed: feature detection, feature tracking, motion detection, and outlier rejection. The choice of point-wise features and outlier rejection based method makes motion detection and stationary obstacle detection efficient. Long run outdoor experiments are conducted in real time and show the effectiveness of the system. / Ph. D.

Animal home range estimations are important for conservation planning and protecting the habitat of threatened species. The accuracy of home range calculations is influenced by the map projection chosen in a geographic information system (GIS) for data analysis. Different methods of projection will distort spatial data in different ways, so it is important to choose a projection that meets the needs of the research. The large number of projections in use today and the lack of distortion comparison between the various types make selecting the most appropriate projection a difficult decision. The purpose of this study is to quantify and compare the amount of area distortion in animal home range estimations when projected into a number of projected coordinate systems in order to understand how the chosen projection influences analysis. The objectives of this research are accomplished by analyzing the tracking data of four species from different regions in North and South America. The home range of each individual from the four species datasets is calculated using the Characteristic Hull Polygon method for home range estimation and then projected into eight projected coordinate systems of various scales and projection type, including equal area, conformal, equidistant, and compromise projections. A continental Albers Equal Area projection is then used as a baseline area for the calculation of a distortion measurement ratio and magnitude of distortion statistic. The distortion measurement ratio and magnitude calculations provide a measurement of the quantity of area distortion caused by a projection. Results show the amount distortion associated with each type of projection method and how the amount of distortion changes for a projection based on geographic location. These findings show how the choice of map projection can have a large influence on data analysis and illustrate the importance of using an appropriate PCS for the needs of a given study. Distorted perceptions can influence decision-making, so it is important to recognize how a map projection can influence the analysis and interpretation of spatial data.

No description available.

The Internet of Things (IoT) has emerged as a powerful tool for meeting our need to collect information about and interact with our environments. One important aspect of this technology is positioning which imposes requirements on both the energy consumption and the arrangement of the systems. For devices that run on battery, low energy consumption is critical and for large deployments, there is a need to minimize the number of devices to reduce cost. Narrowband sub-GHz positioning systems allow for long-range and low-energy functionality at the cost of positioning accuracy compared to wider bandwidth systems. One significant factor that impacts the accuracy of positioning systems is the quality of the radio environment. The aim of this thesis is to investigate methods for using stationary devices in positioning systems for mitigating the effect of the radio environment. The focus lies on a specific IoT narrowband sub-GHz system that use received signal strength indicator and time-of-flight to create range estimates. To meet the objectives, a measurement analysis is performed on a real system deployment. The analysis results in two methods for compensating the range estimations between the devices to locate and the stationary devices in the system. The first method creates a compensation factor based on the measurements from a closely located stationary device. The second method implement and tests six regression models trained on measurements between one or several closely located stationary devices. The results show that both approaches improve the range estimates in the system for two different system deployments. The best method show approximately 76 % improvement on the first deployment and 66 % improvement on the second. The results also show that the training set has to include data from a similar environment for the model to improve the range estimates. Further, for the implemented positioning algorithm, the best methods show no effect on the positioning accuracy in the first deployment and approximately 15 % improvement in the second. / Internet of Things (IoT) möter våra behov av att samla information och interagera med våra miljöer. En viktig aspekt av denna teknik är positionering som ställer krav på både energiförbrukning och systemens anordning. För enheter som körs på batteri är låg energiförbrukning avgörande och för stora distributioner finns ett behov av att minimera antalet enheter för att minimera kostnader. Sub-GHz positioneringssystem med smal bandbredd möjliggör lång räckvidd och låg energiförbrukning till priset av en lägre positioneringsnoggrannhet i jämförelse med system med bredare bandbredd. En viktig faktor som påverkar positioneringsnoggrannheten är kvalitén på radiomiljön. Målet med det här examensarbetet är att undersöka metoder för att använda stationära enheter i positioneringssystem för att mildra effekten av radiomiljön. Fokuset ligger på ett specifikt smalbandigt IoT sub-GHz system som använder sig av mottagen signalstyrka och time-of-flight för att skapa avståndsuppskattningar. För att nå målen utförs en analys av mätdata från en verklig systeminstallation. Analysen resulterar i två metoder för att kompensera avståndsuppskattningar mellan de stationära enheterna och enheterna som ska lokaliseras. Den första metoden skapar en kompenseringsfaktor baserat mätningar från en närliggande stationär enhet. Den andra metoden implementerar och testar sex regressionsmodeller som tränas på mätningar mellan en eller flera närliggande stationära enheter. Resultaten visar att båda metoderna förbättrar avståndsuppskattningarna i systemet för två olika systeminstallationer. Den bästa metoden visar ungefär 76 % förbättring för den första installationen och 66 % förbättring för den andra. Resultaten visar också att träningsdatan måste inkludera data från en liknande miljö för att modellerna ska ge en förbättring av avståndsuppskattningarna. Vidare visas att den bästa metoden för avståndsuppskattningarna inte har någon effekt på positioneringsnoggrannheten för den första installationen och ger en 15 % förbättring för den andra.

Approved for public release, distribution is unlimited / A control system was developed for autonomous visual tracking of a stationary target using a small unmanned aerial vehicle. The kinematic equations of this problem were developed, and the insight obtained from examination was applied in developing controllers for the system. This control system controlled the orientation of the camera to keep it constantly pointing at the target, and also controlled the trajectory of the aircraft in flight around the target. The initial control law that was developed drives the aircraft trajectory to a constant radius around the target. The range to the target is not directly measurable, so it was estimated using steady state Kalman filters. Once a range estimate is obtained, it is used to control the range to the target, and the aircraft trajectory is driven toward a circle with a specified radius. Initial tests of the control system with Simulink simulations have shown good performance of the control system. Further testing with hardware will be conducted, and flight tests are scheduled to be conducted in the near future. Conclusions are drawn and recommendations for further study are presented. / Ensign, United States Navy

Les ondes acoustiques subissent peu de dispersion dans le milieu marin, comparé au milieu aérien. Certaines espèces de cétacés communiquent ainsi à grande distance, d'autres utilisent leurs émissions sonores pour s'orienter. La bioacoustique consiste à étudier ces espèces à partir de l'analyse de leurs sons, c'est-à-dire à les détecter, classer, localiser. Cela peut se faire via un réseau d'hydrophones au déploiement fastidieux. Afin de contribuer au passage à l'échelle de la bioacoustique, cette thèse propose des modèles originaux mono-hydrophone pour l'analyse de ces signaux stationnaires ou transitoires. Premièrement, nous dérivons un nouveau modèle d'estimation de la distance entre une source impulsive (ex. biosonar) et un hydrophone. Notre modèle théorique, l'Intra Spectral ATténuation(ISAT), dérive des lois acoustiques de déformation spectrale du signal transitoire induite par l'atténuation durant sa propagation. Ce modèle relie les rapports énergétiques des bandes de fréquences pondérés par un modèle de perte par atténuation fréquentielle (Thorp ou Leroy) à la distance de propagation. Nous approximons aussi ISAT par un modèle neuromimétique. Ces deux modèles sont validés sur le sonar du cachalot (Physeter macrocephalus) enregistré avec notre bouée acoustique autonome BOMBYX et notre système d'acquisition DECAV en collaboration avec le Parc National de Port-Cros et le sanctuaire Pelagos pour la protection des mammifères marins en Méditerranée. Les mesures d'erreur (RMSE) d'environ 500 mètres sur nos références du centre d'essai OTAN aux Bahamas présentent un intérêt opérationnel. Deuxièmement, nous proposons une analyse originale de l'évolution des voisements de cétacé par codage parcimonieux. Notre encodage des cepstres par apprentissage non supervisé d'un dictionnaire met en évidence l'évolution temporelle des bigrammes des chants que les baleines à bosse mâles émettent durant la période de reproduction. Nous validons ce modèle sur nos enregistrements du canal de Sainte-Marie à Madagascar entre 2008 et 2014, via notre réseau d'hydrophones BAOBAB qui constitue une première dans l'Océan Indien. Nos modèles s'inscrivent dans le projet Scaled Bioacoustics (SABIOD, MI CNRS) et ouvrent de nouvelles perspectives pour les passages à l'échelle temporelle et spatiale de la bioacoustique. / Acoustic waves show low dispersion due to the underwater propagation, compared to the propagation in the air. Some species of cetaceans communicate at long distance, others use their sound production for orientation. The goal of the scientic area called bioacoustics is to study animal species based on the analysis of their emitted sound. Their sounds can be used to detect, to classify and to locate the cetaceans. Recordings can be done with an passive acoustic array of multiple hydrophones, but this method is expensive and difficult to deploy. Thus, in order to scale this approach, we propose in this Phd thesis several original single hydrophone models to analyze these stationary or transient signals.Firstly, we provide a new theoretical model to estimate the distance between the impulsive source (ex. biosonar of the cetacean) and the hydrophone. Our model, the Intra Spectral ATtenuation (ISAT), is based on the spectral signal alteration due to the underwater acoustic propagation, especially the differences in different frequency bands. We also approximated ISAT by an artificial neural network. Both models are validated on clicks emitted by sperm whales (Physeter macrocephalus) recorded by our sonobuoy BOMBYX and our data-acquisition system DECAV developed incollaboration with the National Park of Port-Cros (France) and the Pelagos sanctuary for the protection of marine mammals in the Mediterranean sea (France). The error (RMSE) measures on the recordings of the NATO test center in the Bahamas are about500 meters, promising further real applications. Secondly, we worked on the variations of the cetacean vocalizations using the sparse coding method. The encoding of thecepstrums by unsupervised learning of a dictionary shows bigrammic time changes of the songs of humpback whales (Megaptera novaeangliae). We validate this model on signals recorded in the Ste Marie Channel (Madagascar) between 2008 and 2014, through our network of hydrophones BAOBAB which is the first passive acoustic array deployed in the Indian Ocean.Our models are part of the Saled Bioacoustics project (SABIOD, MI CNRS) and open perspectives for temporal and spatial scaling of bioacoustics.

Passive wireless sensor networks (WSNs) are quickly becoming popular for many applications such as article tracking, position location, temperature sensing, and passive data storage. Passive tags and sensors are unique in that they collect their electrical energy by harvesting it from the ambient environment. Tags with charge pumps collect their energy from the signal they receive from the transmitting source. The efficiency of converting the received signal to DC power is greatly enhanced using a power-optimized waveform (POW). Measurements in the first part of this dissertation show that a POW can provide efficiency gains of up to 12 dB compared to a sine-wave input. Tracking the real-time location of these passive tags is a specialized feature used in some applications such as animal tracking. A passive WSN that uses POWs for the improvement of energy-harvesting may also estimate the range to a tag by measuring the time delay of propagation from the transmitter to the tag and back to the transmitter. The maximum-likelihood (ML) estimator is used for estimating this time delay, which simplifies to taking the cross-correlation of the received signal with the transmitted signal. This research characterizes key aspects of performing range estimations in passive WSNs using POWs. The shape of the POW has a directly-measurable effect on ranging performance. Measurements and simulations show that the RMS bandwidth of the waveform has an inversely proportional relationship to the uncertainty of a range measurement. The clutter of an environment greatly affects the uncertainty and bias exhibited by a range estimator. Random frequency-selective environments with heavy clutter are shown to produce estimation uncertainties more than 20 dB higher than the theoretical lower bound. Estimation in random frequency-flat environments is well-behaved and fits the theory quite nicely. Nonlinear circuits such as the charge pump distort the POW during reflection, which biases the range estimations. This research derives an empirical model for predicting the estimation bias for Dickson charge pumps and verifies it with simulations and measurements.