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Silent Localization of Underwater Sensors Using Magnetometers
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. (Security Link)
2010 (English)In: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, Vol. 2010, no 1Article in journal (Refereed) Published
Abstract [en]

Sensor localization is a central problem for sensor networks. If the sensor positions are uncertain, the target tracking ability of the sensor network is reduced. Sensor localization in underwater environments is traditionally addressed using acoustic range measurements involving known anchor or surface nodes. We explore the usage of triaxial magnetometers and a friendly vessel with known magnetic dipole to silently localize the sensors. The ferromagnetic field created by the dipole is measured by the magnetometers and is used to localize the sensors. The trajectory of the vessel and the sensor positions are estimated simultaneously using an Extended Kalman Filter (EKF). Simulations show that the sensors can be accurately positioned using magnetometers.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2010. Vol. 2010, no 1
Keyword [en]
Underwater sensor localization, Sensor network, Magnetometers, SLAM
National Category
Signal Processing Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-53589DOI: 10.1155/2010/709318ISI: 000274966500001OAI: oai:DiVA.org:liu-53589DiVA: diva2:289902
Projects
MOVIIICADICSLINK-SIC
Available from: 2010-01-25 Created: 2010-01-25 Last updated: 2017-12-12Bibliographically approved
In thesis
1. Topics in Localization and Mapping
Open this publication in new window or tab >>Topics in Localization and Mapping
2011 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The need to determine ones position is common and emerges in many different situations. Tracking soldiers or a robot moving in a building or aiding a tourist exploring a new city, all share the questions ”where is the unit?“ and ”where is the unit going?“. This is known as the localization problem.Particularly, the problem of determining ones position in a map while building the map at the same time, commonly known as the simultaneous localization and mapping problem (slam), has been widely studied. It has been performed in cities using different land bound vehicles, in rural environments using au- tonomous aerial vehicles and underwater for coral reef exploration. In this thesis it is studied how radar signals can be used to both position a naval surface ves- sel but also to simultaneously construct a map of the surrounding archipelago. The experimental data used was collected using a high speed naval patrol boat and covers roughly 32 km. A very accurate map was created using nothing but consecutive radar images.A second contribution covers an entirely different problem but it has a solution that is very similar to the first one. Underwater sensors sensitive to magnetic field disturbances can be used to track ships. In this thesis, the sensor positions them- selves are considered unknown and are estimated by tracking a friendly surface vessel with a known magnetic signature. Since each sensor can track the vessel, the sensor positions can be determined by relating them to the vessel trajectory. Simulations show that if the vessel is equipped with a global navigation satellite system, the sensor positions can be determined accurately.There is a desire to localize firefighters while they are searching through a burn- ing building. Knowing where they are would make their work more efficient and significantly safer. In this thesis a positioning system based on foot mounted in- ertial measurement units has been studied. When such a sensor is foot mounted, the available information increases dramatically since the foot stances can be de- tected and incorporated in the position estimate. The focus in this work has therefore been on the problem of stand still detection and a probabilistic frame- work for this has been developed. This system has been extensively investigated to determine its applicability during different movements and boot types. All in all, the stand still detection system works well but problems emerge when a very rigid boot is used or when the subject is crawling. The stand still detection frame- work was then included in a positioning framework that uses the detected stand stills to introduce zero velocity updates. The system was evaluated using local- ization experiments for which there was very accurate ground truth. It showed that the system provides good position estimates but that the estimated heading can be wrong, especially after quick sharp turns.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. 69 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1489
Keyword
sensor fusion
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-72575 (URN)LiU-TEK-LIC-2011:28 (Local ID)978-91-7393-152-6 (ISBN)LiU-TEK-LIC-2011:28 (Archive number)LiU-TEK-LIC-2011:28 (OAI)
Presentation
2011-05-06, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2011-11-29 Created: 2011-11-28 Last updated: 2011-11-29Bibliographically approved
2. Autonomous Localization in Unknown Environments
Open this publication in new window or tab >>Autonomous Localization in Unknown Environments
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Over the last 20 years, navigation has almost become synonymous with satellite positioning, e.g. the Global Positioning System (GPS). On land, sea or in the air, on the road or in a city, knowing ones position is a question of getting a clear line of sight to enough satellites. Unfortunately, since the signals are extremely weak there are environments the GPS signals cannot reach but where positioning is still highly sought after, such as indoors and underwater. Also, because the signals are so weak, GPS is vulnerable to jamming. This thesis is about alternative means of positioning for three scenarios where gps cannot be used.

Indoors, there is a desire to accurately position first responders, police officers and soldiers. This could make their work both safer and more efficient. In this thesis an inertial navigation system using a foot mounted inertial magnetic mea- surement unit is studied. For such systems, zero velocity updates can be used to significantly reduce the drift in distance travelled. Unfortunately, the estimated direction one is moving in is also subject to drift, causing large positioning errors. We have therefore chosen to throughly study the key problem of robustly estimating heading indoors.

To measure heading, magnetic field measurements can be used as a compass. Unfortunately, they are often disturbed indoors making them unreliable. For estimation support, the turn rate of the sensor can be measured by a gyro but such sensors often have bias problems. In this work, we present two different approaches to estimate heading despite these shortcomings. Our first system uses a Kalman filter bank that recursively estimates if the magnetic readings are disturbed or undisturbed. Our second approach estimates the entire history of headings at once, by matching integrated gyro measurements to a vector of magnetic heading measurements. Large scale experiments are used to evaluate both methods. When the heading estimation is incorporated into our positioning system, experiments show that positioning errors are reduced significantly. We also present a probabilistic stand still detection framework based on accelerometer and gyro measurements.

The second and third problems studied are both maritime. Naval navigation systems are today heavily dependent on GPS. Since GPS is easily jammed, the vessels are vulnerable in critical situations. In this work we describe a radar based backup positioning system to be used in case of GPS failure. radar scans are matched using visual features to detect how the surroundings have changed, thereby describing how the vessel has moved. Finally, we study the problem of underwater positioning, an environment gps signals cannot reach. A sensor network can track vessels using acoustics and the magnetic disturbances they induce. But in order to do so, the sensors themselves first have to be accurately positioned. We present a system that positions the sensors using a friendly vessel with a known magnetic signature and trajectory. Simulations show that by studying the magnetic disturbances that the vessel produces, the location of each sensor can be accurately estimated.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. 73 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1520
Keyword
Localization, indoor positioning, heading estimation, magnetometers
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-91567 (URN)978-91-7519-620-6 (ISBN)
Public defence
2013-06-05, Visionen, B building, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Funder
eLLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsLinnaeus research environment CADICS
Available from: 2013-05-08 Created: 2013-04-26 Last updated: 2013-05-08Bibliographically approved
3. Inertial Navigation and Mapping for Autonomous Vehicles
Open this publication in new window or tab >>Inertial Navigation and Mapping for Autonomous Vehicles
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Navigation and mapping in unknown environments is an important building block for increased autonomy of unmanned vehicles, since external positioning systems can be susceptible to interference or simply being inaccessible. Navigation and mapping require signal processing of vehicle sensor data to estimate motion relative to the surrounding environment and to simultaneously estimate various properties of the surrounding environment. Physical models of sensors, vehicle motion and external influences are used in conjunction with statistically motivated methods to solve these problems. This thesis mainly addresses three navigation and mapping problems which are described below.

We study how a vessel with known magnetic signature and a sensor network with magnetometers can be used to determine the sensor positions and simultaneously determine the vessel's route in an extended Kalman filter (EKF). This is a so-called simultaneous localisation and mapping (SLAM) problem with a reversed measurement relationship.

Previously determined hydrodynamic models for a remotely operated vehicle (ROV) are used together with the vessel's sensors to improve the navigation performance using an EKF. Data from sea trials is used to evaluate the system and the results show that especially the linear velocity relative to the water can be accurately determined.

The third problem addressed is SLAM with inertial sensors, accelerometers and gyroscopes, and an optical camera contained in a single sensor unit. This problem spans over three publications.

We study how a SLAM estimate, consisting of a point cloud map, the sensor unit's three dimensional trajectory and speed as well as its orientation, can be improved by solving a nonlinear least-squares (NLS) problem. NLS minimisation of the predicted motion error and the predicted point cloud coordinates given all camera measurements is initialised using EKF-SLAM.

We show how NLS-SLAM can be initialised as a sequence of almost uncoupled problems with simple and often linear solutions. It also scales much better to larger data sets than EKF-SLAM. The results obtained using NLS-SLAM are significantly better using the proposed initialisation method than if started from arbitrary points. A SLAM formulation using the expectation maximisation (EM) algorithm is proposed. EM splits the original problem into two simpler problems and solves them iteratively. Here the platform motion is one problem and the landmark map is the other. The first problem is solved using an extended Rauch-Tung-Striebel smoother while the second problem is solved with a quasi-Newton method. The results using EM-SLAM are better than NLS-SLAM both in terms of accuracy and complexity.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. 77 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1623
Keyword
SLAM, Inertial Navigation, Filtering, Smoothing, Cameras, Optimisation, Autonomous
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-110373 (URN)10.3384/diss.diva-110373 (DOI)9789175192338 (ISBN)
Public defence
2014-10-17, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Projects
LINK-SIC
Funder
VINNOVA
Available from: 2014-09-17 Created: 2014-09-09 Last updated: 2017-01-19Bibliographically approved

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Callmer, JonasSkoglund, MartinGustafsson, Fredrik

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