GPS-derived geoid using artificial neural network and least squares collocation

The geoidal undulations are needed for determining the orthometric heights from the Global Positioning System GPS-derived ellipsoidal heights. There ore several methods for geoidal undulation determination. The paper presents a method employing the Artificial Neural Network (ANN) approximation together with the Least Squares Collocation (LSC). The surface obtained by the ANN approximation is used as a trend surface in the least squares collocation. In numerical examples four surfaces were compared: the global geopotential model (EGM96), the European gravimetric quasigeoid 1997 (EGG97), the surface approximated with minimum curvature splines in tension algorithm and the ANN surface approximation. The effectiveness of the ANN surface approximation depends on the number of control points. If the number of well-distributed control points is sufficiently large, the results are better than those obtained by the minimum curvature algorithm and comparable to those obtained by the EGG97 model

Visual Odometry and Trajectory Reconstruction for UAVs

The growing popularity of systems based on Unmanned Aerial Vehicles (UAVs) is highlighting their vulnerability particularly in relation to the positioning system used. Typically, UAV architectures use the civilian GPS which is exposed to a number of different attacks, such as jamming or spoofing. This is why it is important to develop alternative methodologies to accurately estimate the actual UAV position without relying on GPS measurements only. In this paper we propose a position estimate method for UAVs based on monocular visual odometry. We have developed a flight control system capable of keeping track of the entire trajectory travelled, with a reduced dependency on the availability of GPS signal. Moreover, the simplicity of the developed solution makes it applicable to a wide range of commercial drones. The final goal is to allow for safer flights in all conditions, even under cyber-attacks trying to deceive the drone

Smart environment monitoring through micro unmanned aerial vehicles

In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection

Immunity-Based Framework for Autonomous Flight in GPS-Challenged Environment

In this research, the artificial immune system (AIS) paradigm is used for the development of a conceptual framework for autonomous flight when vehicle position and velocity are not available from direct sources such as the global navigation satellite systems or external landmarks and systems. The AIS is expected to provide corrections of velocity and position estimations that are only based on the outputs of onboard inertial measurement units (IMU). The AIS comprises sets of artificial memory cells that simulate the function of memory T- and B-cells in the biological immune system of vertebrates. The innate immune system uses information about invading antigens and needed antibodies. This information is encoded and sorted by T- and B-cells. The immune system has an adaptive component that can accelerate and intensify the immune response upon subsequent infection with the same antigen. The artificial memory cells attempt to mimic these characteristics for estimation error compensation and are constructed under normal conditions when all sensor systems function accurately, including those providing vehicle position and velocity information. The artificial memory cells consist of two main components: a collection of instantaneous measurements of relevant vehicle features representing the antigen and a set of instantaneous estimation errors or correction features, representing the antibodies. The antigen characterizes the dynamics of the system and is assumed to be correlated with the required corrections of position and velocity estimation or antibodies. When the navigation source is unavailable, the currently measured vehicle features from the onboard sensors are matched against the AIS antigens and the corresponding corrections are extracted and used to adjust the position and velocity estimation algorithm and provide the corrected estimation as actual measurement feedback to the vehicle’s control system. The proposed framework is implemented and tested through simulation in two versions: with corrections applied to the output or the input of the estimation scheme. For both approaches, the vehicle feature or antigen sets include increments of body axes components of acceleration and angular rate. The correction feature or antibody sets include vehicle position and velocity and vehicle acceleration adjustments, respectively. The impact on the performance of the proposed methodology produced by essential elements such as path generation method, matching algorithm, feature set, and the IMU grade was investigated. The findings demonstrated that in all cases, the proposed methodology could significantly reduce the accumulation of dead reckoning errors and can become a viable solution in situations where direct accurate measurements and other sources of information are not available. The functionality of the proposed methodology and its promising outcomes were successfully illustrated using the West Virginia University unmanned aerial system simulation environment

Passive acoustic method for aircraft localization

The present thesis investigates a passive acoustic method to locate maneuvering aircraft. The method is based on the acoustical Doppler effect, as a particular effect of the signals received by a mesh of spatially distributed microphones. A one-dimensional version of the ambiguity function allows for the calculation of the frequency stretch factor that occurs between the sound signals received by a pair of microphones. The mathematical expression for this frequency stretch is a function of the aircraft position and velocity, both of them being estimated by a genetic algorithm. The method requires only a minimum of seven microphones and the prior knowledge of the aircraft position and velocity at a given time. The advantages of the method are that it is suitable for all kind of aircraft, not only propeller-driven, and is not restricted to low heights above the ground. Its applicability could be, for instance, to supplement aircraft noise monitoring systems or to supervise small airports activities. This doctoral research includes the theoretical background of the method as well as the detailed description of its implementation. To assess the performance of the method, results from computer simulations are discussed. First of all, noise propagation is considered in a lossless medium, thus only geometrical spreading influences the sound emitted by the source traveling to the receivers. The accuracy of each step of the method has been evaluated and the results obtained reveal acceptable performance. Due to the large distances between microphones and the aircraft in flight, the atmospheric attenuation plays a major roll. Therefore, computer simulations have also been carried out under the assumption of an homogeneous but non lossless medium to evaluate the influence of the atmospheric absorption on the aircraft location. Under these conditions, the performance of the method with respect to the microphone distribution is discussed. Moreover, the location method has also been tested for a possible inaccuracy on the microphones synchronization. Finally, an outdoor experimental validation of the acoustic method has been carried out with a radio control airplane. The description of the experimental test is detailed in the present work as well as the results obtained.La tesi desenvolupa, implementa i valida un mètode acústic per a la localització d’aeronaus. El mètode es basa en l’efecte Doppler que es percep en els registres de diferents micròfons distribuïts al voltant d’un aeroport. La versió u-dimensional de la funció d’ambigüitat permet el còmput del factor de compressió o expansió que sorgeix entre els registres freqüencials d’ un parell de micròfons. Aquest factor Freqüencial es pot expressar matemàticament en funció de la posició i velocitat de l’aeronau, que s’estimen en aquesta tesi a partir d’algoritmes genètics. El mètode només requereix de set micròfons i el coneixement previ de la posició de l’avió en un moment donat. Els principals avantatges del mètode són que és un mètode vàlid per qualsevol tipus d’aeronau, no només per avions d’hèlix o helicòpters, i que no restringeix a vols de baixa alçada. La seva aplicació podria ser, per exemple, complementar un sistema de monitorització de soroll aeri o bé supervisar l’activitat dels aeroports petits que no disposen de sistemes de radar. Aquesta investigació inclou el desenvolupament teòric del mètode així com la descripció detallada de la seva implementació. Per tal d’avaluar l’efectivitat del mètode, es presenten i analitzen resultats obtinguts a partir de diverses simulacions. Com a primer cas, es considera que el so es propaga en un medi conservatiu, és a dir, el so que es propaga des de la font fins als receptors només es veu afectat per l’atenuació geomètrica. Sota aquest model senzill de propagació, s’ha analitzat l’accuracy de cada un dels passos del mètode i els resultats obtinguts posen de manifest una bona ... del mètode. Tenint en compte que les distàncies entre els micròfons i l’avió en vol són llargues, l’atenuació atmosfèrica influeix també en la propagació del so emès per l’avió. Per tant, el segon cas de simulacions que s’han dut a terme considera un medi de propagació homogeni i no conservatiu amb l’objectiu d’avaluar la influència de l’atenuació atmosfèrica en la localització acústica de l’aeronau. Sota aquestes condicions, també s’ha analitzat l’eficàcia del mètode en funció de la distribució de micròfons. A més, el mètode de localització s’ha posat a prova sota possibles errors en la sincronització dels set micròfons. Finalment, s’ha dut a terme una validació experimental del mètode amb una avioneta de radio control al Club Aeronàutic Egara. La descripció d’aquest test experimental es detalla en la tesis així com els resultats obtinguts que demostren la validesa satisfactòria del mètode

Space-partitioning with cascade-connected ANN structures for positioning in mobile communication systems

The world around us is getting more connected with each day passing by – new portable devices employing wireless connections to various networks wherever one might be. Locationaware computing has become an important bit of telecommunication services and industry. For this reason, the research efforts on new and improved localisation algorithms are constantly being performed. Thus far, the satellite positioning systems have achieved highest popularity and penetration regarding the global position estimation. In spite the numerous investigations aimed at enabling these systems to equally procure the position in both indoor and outdoor environments, this is still a task to be completed. This research work presented herein aimed at improving the state-of-the-art positioning techniques through the use of two highly popular mobile communication systems: WLAN and public land mobile networks. These systems already have widely deployed network structures (coverage) and a vast number of (inexpensive) mobile clients, so using them for additional, positioning purposes is rational and logical. First, the positioning in WLAN systems was analysed and elaborated. The indoor test-bed, used for verifying the models’ performances, covered almost 10,000m2 area. It has been chosen carefully so that the positioning could be thoroughly explored. The measurement campaigns performed therein covered the whole of test-bed environment and gave insight into location dependent parameters available in WLAN networks. Further analysis of the data lead to developing of positioning models based on ANNs. The best single ANN model obtained 9.26m average distance error and 7.75m median distance error. The novel positioning model structure, consisting of cascade-connected ANNs, improved those results to 8.14m and 4.57m, respectively. To adequately compare the proposed techniques with other, well-known research techniques, the environment positioning error parameter was introduced. This parameter enables to take the size of the test environment into account when comparing the accuracy of the indoor positioning techniques. Concerning the PLMN positioning, in-depth analysis of available system parameters and signalling protocols produced a positioning algorithm, capable of fusing the system received signal strength parameters received from multiple systems and multiple operators. Knowing that most of the areas are covered by signals from more than one network operator and even more than one system from one operator, it becomes easy to note the great practical value of this novel algorithm. On the other hand, an extensive drive-test measurement campaign, covering more than 600km in the central areas of Belgrade, was performed. Using this algorithm and applying the single ANN models to the recorded measurements, a 59m average distance error and 50m median distance error were obtained. Moreover, the positioning in indoor environment was verified and the degradation of performances, due to the crossenvironment model use, was reported: 105m average distance error and 101m median distance error. When applying the new, cascade-connected ANN structure model, distance errors were reduced to 26m and 2m, for the average and median distance errors, respectively. The obtained positioning accuracy was shown to be good enough for the implementation of a broad scope of location based services by using the existing and deployed, commonly available, infrastructure

Ambiguity Function Method Scheme for Aircraft Attitude Sensor Utilising GPS/GLONASS Carrier Phase Measurement

When the receivers of GPS, GLONASS, COMPASS and other such systems are equipped with multiple antennas, they can give attitude information. Based on the difference carrier phase equations established in local level frame (LLF), a new algorithm is presented to resolve aircraft attitude determination problems in real-time. Presuming that the cycle integer ambiguity is known, the measurement equations have attitude analytical resolutions using single difference (SD) equations of two navigation satellites in-view. Similar with SD process, the doubledifference (DD) measurements are established and analysed. In addition, the SD and DD algorithms are capable of reducing the integer search space into some discrete point space and then the ambiguity function method (AFM) resolves the ambiguity function within the point solutions space. Therefore the procedures have very low computation, thus saving time. The hardware architecture has been realised using multiple  GPS/GLONASS OEMs. The experimental results have demonstrated that the proposed approach is effective and can satisfy the requirement of real-time application in cases of GPS, and combined GPS, and GLONASS.Defence Science Journal, 2009, 59(5), pp.466-470, DOI:http://dx.doi.org/10.14429/dsj.59.154

Posture optimization algorithm for large structure assemblies based on skin model

Geometric deviations inevitably occur in product manufacturing and seriously affect the assembly quality and product functionality. Assembly simulations on the basis of computer-aided design (CAD) package could imitate the assembly process and thus find out the design deficiencies and detect the assemblability of the components. Although lots of researches have been done on the prediction of assembly variation considering the geometric errors, most of them only simplify the geometric variation as orientation and position deviation rather than the manufacturing deformation. However, in machinery manufacturing, even if the manufacturing defects are limited, they could propagate and accumulate through components and lead to a noncompliant assembly. Recently, many point-based models have been applied to assembly simulation; however they are mainly interested in simulating the resulting positions of the assembled parts and lack the consideration of the postprocessing after positioning. This paper enriches the complete assembly simulation process based on skin model and presents a simple and effective posture evaluation and optimization method. The studied approach includes a software algorithm applied to evaluate the contact state of the assembly parts and a mathematical model based on the particle swarm optimization to acquire the optimal assembly posture. To verify the efficiency and feasibility of the proposed method, a case study on the aircraft wing box scaling model assembly is performed

Identification and Optimal Linear Tracking Control of ODU Autonomous Surface Vehicle

Autonomous surface vehicles (ASVs) are being used for diverse applications of civilian and military importance such as: military reconnaissance, sea patrol, bathymetry, environmental monitoring, and oceanographic research. Currently, these unmanned tasks can accurately be accomplished by ASVs due to recent advancements in computing, sensing, and actuating systems. For this reason, researchers around the world have been taking interest in ASVs for the last decade. Due to the ever-changing surface of water and stochastic disturbances such as wind and tidal currents that greatly affect the path-following ability of ASVs, identification of an accurate model of inherently nonlinear and stochastic ASV system and then designing a viable control using that model for its planar motion is a challenging task. For planar motion control of ASV, the work done by researchers is mainly based on the theoretical modeling in which the nonlinear hydrodynamic terms are determined, while some work suggested the nonlinear control techniques and adhered to simulation results. Also, the majority of work is related to the mono- or twin-hull ASVs with a single rudder. The ODU-ASV used in present research is a twin-hull design having two DC trolling motors for path-following motion. A novel approach of time-domain open-loop observer Kalman filter identifications (OKID) and state-feedback optimal linear tracking control of ODU-ASV is presented, in which a linear state-space model of ODU-ASV is obtained from the measured input and output data. The accuracy of the identified model for ODU-ASV is confirmed by validation results of model output data reconstruction and benchmark residual analysis. Then, the OKID-identified model of the ODU-ASV is utilized to design the proposed controller for its planar motion such that a predefined cost function is minimized using state and control weighting matrices, which are determined by a multi-objective optimization genetic algorithm technique. The validation results of proposed controller using step inputs as well as sinusoidal and arc-like trajectories are presented to confirm the controller performance. Moreover, real-time water-trials were performed and their results confirm the validity of proposed controller in path-following motion of ODU-ASV