Controlling the shape and scale of triangular formations using landmarks and bearing-only sensing

© 2016 TCCT. This work considers the scenario where three agents that can sense only bearings use two landmarks to control their formation shape. We define a method of relating the known distance separating the landmarks back to the edge lengths of the triangular formation. The result is used to define a formation control law that incorporates inter-agent distance constraints. We prove a strong exponential convergence result and show how one can extend the controller such that global stability from any initial position is possible

Symmetry of Magnetostatic Fields Generated by Toroidal Helicoidal Magnets

1noIn this paper the axial symmetry of the magnetic field generated by a permanent magnet of helicoidal toroidal kind is shown. In the first part of the paper we illustrate the shape of the magnet and the number of areas where the field is calculated to demonstrate the symmetry. We define quantitatively the size of the toroidal helical magnet and the regions where the magnetostatic field is evaluated. The field is carried out for each angular sector that represents the regions where the magnetic flux density is computed. This calculation is performed with reference to a matrix of points belonging to each sector. Two sets of evaluations are performed. The first one is referred to a less dense matrix of points relative to all the regions. The aim of this computation is to demonstrate the axial symmetry of the field. The second set of calculations concerns the field evaluation by using a much higher dense matrix of points. By using this data we are able to interpolate the same field with a high precision. This second evaluation of the field is carried out with reference only to the flat region facing the first coil of the helical toroidal magnet. The use of an interpolation surface through the final points of the magnetic induction vectors previously computed allows a very fast evaluation of the field virtually in all the infinite points of the angular sector. The symmetry enables us to drastically reduce the time computation of the magnetostatic field in the points of interest.partially_openopenMuscia, RobertoMuscia, Robert

Node localization in underwater sensor networks (UWSN)

This dissertation focuses on node localization in underwater wireless sensor networks (UWSNs) where anchor nodes have knowledge of their own locations and communicate with sensor nodes in acoustic or magnetic induction (MI) means. The sensor nodes utilize the communication signals and the locations of anchor nodes to locate themselves and propagate their locations through the network. For UWSN using MI communications, this dissertation proposes two localization methods: rotation matrix (RM)-based method and the distance-based method. Both methods require only two anchor nodes with arbitrarily oriented tri-directional coils to locate one sensor node in the 3-D space, thus having advantages in a sparse network. Simulation studies show that the RM-based method achieves high localization accuracy, while the distance-based method exhibits less computational complexity. For UWSN using acoustic communications, this dissertation proposes a novel multi-hop node localization method in the 2-D and 3-D spaces, respectively. The proposed method estimates Euclidean distances to anchor nodes via multi-hop propagations with the help of angle of arrival (AoA) measurements. Simulation results show that the proposed method achieves better localization accuracy than existing multi-hop methods, with high localization coverage. This dissertation also investigates the hardware implementation of acoustic transmitter and receiver, and conducted field experiments with the hardware to estimate ToA using single pseudo-noise (PN) and dual PN(DPN) sequences. Both simulation and field test results show that the DPN sequences outperform the single PNs in severely dispersive channels and when the carrier frequency offset (CFO) is high --Abstract, page iv

Improvement Metrological Characteristics of the Antenna System Using Smart Angle Sensor

Techniques for the improvement of the metrological and functional characteristics of the tracking antenna system due to designed optoelectronic smart angle sensor with built-in functions of detection and compensation of errors in mechanical constructions is considered. Performance characteristics and results of experimental research of dual-channel optoelectronic angle sensor are describe

Algorithm for Geodetic Positioning Based On Angle-Of-Arrival of Automatic Dependent Surveillance-Broadcasts

This paper develops a non-precision, three-dimensional, geodetic positioning algorithm for airborne vehicles. The algorithm leverages the proliferation of Automatic Dependent Surveillance – Broadcast (ADS-B) equipped aircraft, utilizing them as airborne navigation aids to generate an RF Angle-of-Arrival (AOA) and Angle-of-Elevation (AOE) based geodetic position. The resulting geodetic position can serve as a redundant navigation system for use during locally limited Global Navigation Satellite System (GNSS) availability, be used to validate on-board satellite navigation systems in an effort to detect local spoofing attempts, and be used to validate ADS-B position reports. The navigation algorithm is an implementation of an Extended Kalman Filter (EKF) that is loosely based on Simultaneous Localization and Mapping (SLAM), in that it tracks ADS-B capable aircraft while simultaneously determining the geodetic position and velocity of the host vehicle. Unlike SLAM, where the absolute location – latitude/longitude – of the landmarks is unknown and must be estimated as the vehicle encounters them, the absolute position of the airborne navigation aids is typically well-known and periodically reported in the ADS-B data set. Because the absolute position of the navigation aids are known, the resulting host vehicle position will also be an absolute, rather than a relative position. Secondarily, the continuous tracking of the airborne navigation aids allows reported ADS-B positions to be validated against the estimated navigation aid position; thereby, concurrently accomplishing ADS-B validation and host vehicle geolocation. This research has demonstrated through a series of simulated Monte-Carlo tests that the algorithm is capable of generating valid position estimates, along with a reliable estimate of its accuracy, across a variety of anticipated input conditions. With multiple GNSS quality navigation aids available, mean position errors below 225 meters were observed. As the quality of the navigation aids decreased, so too did the accuracy of the algorithm. Utilizing navigation aids with an accuracy of 4 nautical miles (95% containment) resulted in mean position errors on the order of 0.75 nautical miles. These results demonstrate that the method is feasible, and even under worst case conditions, the accuracy of the position estimate generated by the algorithm was sufficient to allow an aircraft to navigate to its destination

Analyzing Space-Based Interferometric Measurements of Stars and Network Measurements of Gamma-Ray Bursts

Since the announcement of the discovery of sources of bursts of gamma-ray radiation in 1973, hundreds more reports of such bursts have now been published. Numerous artificial satellites have been equipped with gamma-ray detectors including the very successful Compton Gamma Ray Observatory BATSE instrument. Unfortunately, we have made no progress in identifying the source(s) of this high energy radiation. We suspected that this was a consequence of the method used to define gamma-ray burst source "error boxes." An alternative procedure to compute gamma-ray burst source positions, with a purely physical underpinning, was proposed in 1988 by Taff. Since then we have also made significant progress in understanding the analytical nature of the triangulation problem and in computing actual gamma-ray burst positions and their corresponding error boxes. For the former, we can now mathematically illustrate the crucial role of the area occupied by the detectors, while for the latter, the Atteia et al. (1987) catalog has been completely re-reduced. There are very few discrepancies in locations between our results and those of the customary "time difference of arrival" procedure. Thus, we have numerically demonstrated that the end result, for the positions, of these two very different-looking procedures is the same. Finally, for the first time, we provide a sample of realistic "error boxes" whose non-simple shapes vividly portray the difficulty of burst source localization

HETEROGENEOUS MULTI-SENSOR FUSION FOR 2D AND 3D POSE ESTIMATION

Sensor fusion is a process in which data from different sensors is combined to acquire an output that cannot be obtained from individual sensors. This dissertation first considers a 2D image level real world problem from rail industry and proposes a novel solution using sensor fusion, then proceeds further to the more complicated 3D problem of multi sensor fusion for UAV pose estimation. One of the most important safety-related tasks in the rail industry is an early detection of defective rolling stock components. Railway wheels and wheel bearings are two components prone to damage due to their interactions with the brakes and railway track, which makes them a high priority when rail industry investigates improvements to current detection processes. The main contribution of this dissertation in this area is development of a computer vision method for automatically detecting the defective wheels that can potentially become a replacement for the current manual inspection procedure. The algorithm fuses images taken by wayside thermal and vision cameras and uses the outcome for the wheel defect detection. As a byproduct, the process will also include a method for detecting hot bearings from the same images. We evaluate our algorithm using simulated and real data images from UPRR in North America and it will be shown in this dissertation that using sensor fusion techniques the accuracy of the malfunction detection can be improved. After the 2D application, the more complicated 3D application is addressed. Precise, robust and consistent localization is an important subject in many areas of science such as vision-based control, path planning, and SLAM. Each of different sensors employed to estimate the pose have their strengths and weaknesses. Sensor fusion is a known approach that combines the data measured by different sensors to achieve a more accurate or complete pose estimation and to cope with sensor outages. In this dissertation, a new approach to 3D pose estimation for a UAV in an unknown GPS-denied environment is presented. The proposed algorithm fuses the data from an IMU, a camera, and a 2D LiDAR to achieve accurate localization. Among the employed sensors, LiDAR has not received proper attention in the past; mostly because a 2D LiDAR can only provide pose estimation in its scanning plane and thus it cannot obtain full pose estimation in a 3D environment. A novel method is introduced in this research that enables us to employ a 2D LiDAR to improve the full 3D pose estimation accuracy acquired from an IMU and a camera. To the best of our knowledge 2D LiDAR has never been employed for 3D localization without a prior map and it is shown in this dissertation that our method can significantly improve the precision of the localization algorithm. The proposed approach is evaluated and justified by simulation and real world experiments