Localization Based on Parallel Robots Kinematics as an Alternative to Trilateration

In this work a new scheme for range-based localization is proposed. The main goal is to estimate the position of a mobile point based on distance measurements from fixed devices, called anchors, and on inertial measurements. Due to the non-linear nature of the problem, an analytic relation to compute the position starting from these measurements does not exist, and often trilateration methods are used, generally based on least-square algorithms. The proposed scheme is based on the modelling of the localization process as a parallel robot, thereby methodologies and control algorithms used in the robotic area can be exploited. In particular, a closed loop control system is designed for tracking the position of a mobile point based on range measurements from fixed anchors, and it is shown a peculiar structure of the tracking error dynamics, whose allows an intuitive gain tuning and ensures global exponential stability. Moreover, it is also shown a nice connection between tuning parameters and rate of convergence of the estimation error. Experimental results confirm the validity of the proposed localization method

Context Exploitation in Data Fusion

Complex and dynamic environments constitute a challenge for existing tracking algorithms. For this reason, modern solutions are trying to utilize any available information which could help to constrain, improve or explain the measurements. So called Context Information (CI) is understood as information that surrounds an element of interest, whose knowledge may help understanding the (estimated) situation and also in reacting to that situation. However, context discovery and exploitation are still largely unexplored research topics. Until now, the context has been extensively exploited as a parameter in system and measurement models which led to the development of numerous approaches for the linear or non-linear constrained estimation and target tracking. More specifically, the spatial or static context is the most common source of the ambient information, i.e. features, utilized for recursive enhancement of the state variables either in the prediction or the measurement update of the filters. In the case of multiple model estimators, context can not only be related to the state but also to a certain mode of the filter. Common practice for multiple model scenarios is to represent states and context as a joint distribution of Gaussian mixtures. These approaches are commonly referred as the join tracking and classification. Alternatively, the usefulness of context was also demonstrated in aiding the measurement data association. Process of formulating a hypothesis, which assigns a particular measurement to the track, is traditionally governed by the empirical knowledge of the noise characteristics of sensors and operating environment, i.e. probability of detection, false alarm, clutter noise, which can be further enhanced by conditioning on context. We believe that interactions between the environment and the object could be classified into actions, activities and intents, and formed into structured graphs with contextual links translated into arcs. By learning the environment model we will be able to make prediction on the target\u2019s future actions based on its past observation. Probability of target future action could be utilized in the fusion process to adjust tracker confidence on measurements. By incorporating contextual knowledge of the environment, in the form of a likelihood function, in the filter measurement update step, we have been able to reduce uncertainties of the tracking solution and improve the consistency of the track. The promising results demonstrate that the fusion of CI brings a significant performance improvement in comparison to the regular tracking approaches

Stochastic Real-time Optimal Control: A Pseudospectral Approach for Bearing-Only Trajectory Optimization

A method is presented to couple and solve the optimal control and the optimal estimation problems simultaneously, allowing systems with bearing-only sensors to maneuver to obtain observability for relative navigation without unnecessarily detracting from a primary mission. A fundamentally new approach to trajectory optimization and the dual control problem is developed, constraining polynomial approximations of the Fisher Information Matrix to provide an information gradient and allow prescription of the level of future estimation certainty required for mission accomplishment. Disturbances, modeling deficiencies, and corrupted measurements are addressed with recursive updating of the target estimate with an Unscented Kalman Filter and the optimal path with Radau pseudospectral collocation methods and sequential quadratic programming. The basic real-time optimal control (RTOC) structure is investigated, specifically addressing limitations of current techniques in this area that lose error integration. The resulting guidance method can be applied to any bearing-only system, such as submarines using passive sonar, anti-radiation missiles, or small UAVs seeking to land on power lines for energy harvesting. Methods and tools required for implementation are developed, including variable calculation timing and tip-tail blending for potential discontinuities. Validation is accomplished with simulation and flight test, autonomously landing a quadrotor helicopter on a wire

Nonlinear State Estimation and Control of Autonomous Aerial Robots: Design and Experimental Validation of Smartphone Based Quadrotor

This work presents developments of Guidance, Navigation and Control (GNC) systems with application to autonomous Unmanned Aerial Vehicle (UAV). Precisely, this work shows the development of navigation system based on nonlinear complementary filters for position, velocity and attitude estimation using low-cost sensors. The proposed filtering method provides attitude estimates in quaternion representations and position and velocity estimates by fusing measurements from Inertial Measurement Unit (IMU), GPS, and a barometer. Least Square Method (LSM) was used in gains tuning to find the best-fitting of the estimated states with precise measurements obtained by a vision based motion capture system. A complete navigation system was produced by integrating both the attitude and the position filters. The integration of the filtering approach based primarily on the ease of design and computational load. Furthermore, the structure of the filtering design allow for straightforward implementation without a need of high performance signal processing. Moreover, the filters can be tuned totally independent of each other. This work also introduces a nonlinear flight controller for stability and trajectory tracking that is practical for real-time implementation. This controller is also demonstrated the ability of a supervisory controller to provide effective waypoint navigation capabilities in autonomous UAV. The implementation of the guidance, navigation, and control algorithms were adopted in the design of a novel smartphone based autopilot for particular quadrotor aerial platforms. The performances of the proposed work are then evaluated by means of several flight tests. The work also includes a design of advanced navigation and guidance systems based on Robot Operating System (ROS) for Search And Rescue (SAR) missions. Primarily, the performance of the navigation and guidance systems were tested in laboratory by simulating GPS measurements in Linux computer mounted on the top of a quadrotor. This activity facilitates moving by the experiments from indoor to outdoor

Wireless indoor positioning based on TDOA and DOA estimation techniques using IEEE 802.11 standards

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2015von Abdo Nasser Ali Gabe

Laser Based Altimetry for Unmanned Aerial Vehicle Hovering Over a Snow Surface

A microwave radar for non-invasive snow stratigraphy measurements has been developed. Results were promising, but it failed to detect light powder snow in the air-snowpack interface. The aim of this thesis is to find and verify a system for estimating altitude on centimeter scale over a snow surface, independent of snow conditions. Also, relative pitch and roll angle estimation between the UAV and local surface should be resolved, to help directing the radar beam perpendicularly to the surface. After a variety of technical solutions were examined, we propose a system of three time-of-flight near-infrared altimeters pointing at different directions towards the surface. Experimental results showed RMS error of 1.39 cm for range estimation averaged over the most common snow types, and 2.81 cm for wet snow, which was the least reflective medium. An experiment conducted for an array of two altimeters scanning over a snow surface, showed that the local, relative surface tilt was found to be accurate within ±2° given that it was sufficiently planar. Further, the altitude RMS error was estimated to 1.57 cm. We conclude that the chosen altimeter was within the requirements, and that an array of three altimeters would give acceptable relative tilt estimation in to planes on the snow surface. The system should be subject to flight testing and implemented on UAV platform such that it can aid the microwave radar system during snow scanning

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

Hybrid Observer for Indoor Localization with Random Time-of-Arrival Measurments

In this work an indoor position estimation algorithm will be proposed. The position will be measured by means of a sensor network composed by fixed beacons placed on the indoor environment and a mobile beacon mounted on the object to be tracked. The mobile beacon communicates with all the fixed beacons by means of ultra wide-band signals, and the distance between them is computed by means of time of flight techniques. Moreover, inertial measurements will be used when the position measurements are not available. Two main problems will be considered in the proposed architecture: the fact that the beacons work with a lower update rate than the IMU, and that the mobile beacon can comunicate with only one fixed beacon at a time. Experimental results will be shown in order to validate the effectiveness of the proposed technique