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Autonomous Localization in Unknown Environments
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
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 [en]
Localization, indoor positioning, heading estimation, magnetometers
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-91567ISBN: 978-91-7519-620-6 (print)OAI: oai:DiVA.org:liu-91567DiVA: diva2:618300
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
List of papers
1. Robust Heading Estimation Indoors
Open this publication in new window or tab >>Robust Heading Estimation Indoors
2013 (English)Report (Other academic)
Abstract [en]

Indoor positioning in unknown environments is crucial for rescue personnel and future infotainment systems. Dead-reckoning inertial sensor data gives accurate estimate of distance, for instance using zero velocity updates, while the heading estimation problem is inherently more difficult due to the large degree of magnetic disturbances indoors. We propose a Kalman filter bank approach based on supporting a magnetic compass with gyroscope turn rate information, where a hidden Markov model is used to model the presence of magnetic disturbances. In parallel, we suggest to run a robust heading estimation system based on data from a sliding window. The robust estimate is used to detect filter divergence, and to restart the filter when needed. The underlying assumptions and the heading estimation performance are supported in field trials using more than 500 data sets from more than 50 venues in 5 continents.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. 13 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3061
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-91393 (URN)
Funder
eLLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsLinnaeus research environment CADICS
Available from: 2013-04-23 Created: 2013-04-23 Last updated: 2013-05-08
2. Robust Heading Estimation Indoors using Convex Optimization
Open this publication in new window or tab >>Robust Heading Estimation Indoors using Convex Optimization
2013 (English)Report (Other academic)
Abstract [en]

The problem of estimating heading is central in the indoor positioning problem based on mea- surements from inertial measurement and magnetic units. Integrating rate of turn angular rate gives the heading with unknown initial condition and a linear drift over time, while the magnetometer gives absolute heading, but where long segments of data are useless in prac- tice because of magnetic disturbances. A basic Kalman filter approach with outlier rejection has turned out to be difficult to use with high integrity. Here, we propose an approach based on convex optimization, where segments of good magnetometer data are separated from disturbed data and jointly fused with the yaw rate measurements. The optimization framework is flexible with many degrees of freedom in the modeling phase, and we outline one design. A recursive solution to the optimization is derived, which has a computational complexity comparable to the simplest possible Kalman filter. The performance is evaluated using data from a handheld smartphone for a large amount of indoor trajectories, and the result demonstrates that the method effectively resolves the magnetic disturbances.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. 9 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3060
Keyword
Heading estimation, magnetometer, gyro, disturbances, optimization
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-91392 (URN)LiTH-ISY-R-3060 (ISRN)
Funder
eLLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsLinnaeus research environment CADICS
Available from: 2013-04-23 Created: 2013-04-23 Last updated: 2014-06-16Bibliographically approved
3. An Inertial Navigation Framework for Indoor Positioning with Robust Heading
Open this publication in new window or tab >>An Inertial Navigation Framework for Indoor Positioning with Robust Heading
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Indoor localization in unknown environments is considered, using inertial measurements from accelerometers, gyroscopes and magnetometers. Foot-mounted inertial sensors allow for stand-still detection triggering zero velocity updates that reduces the inertial navigation system (ins) drift in distance traveled from cubical to linear in time. We present a statistical framework, based on an navigation model. The standard stand-still mode is complemented with binary modes of magnetic disturbances. Test statistics for these two mode estimation problems are derived. Instead of making hard decisions, a hidden Markov model filter is used to compute the mode probabilities, leading to soft measurement updates in the Kalman filter.

Based on this, a robust smoothed heading estimate is computed in a second stage using the magnetometer. The final position estimate is then obtained by fusing the ins output with the robust heading in a standard dead-reckoning filter. Experiments demonstrate that the robust heading decreases the relative error in position from 10% to less than 1%, despite large magnetic disturbances.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-92131 (URN)
Available from: 2013-05-08 Created: 2013-05-08 Last updated: 2013-05-08
4. RADAR SLAM using Visual Features
Open this publication in new window or tab >>RADAR SLAM using Visual Features
Show others...
2011 (English)In: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, Vol. 2011, no 71Article in journal (Refereed) Published
Abstract [en]

A vessel navigating in a critical environment such as an archipelago, requires very accurate movement estimates. Intentional or unintentional jamming makes gps unreliable as the only source of information and an additional independent navigation system should be used. In this paper we suggest estimating the vessel movements using a sequence of radar images from the preexisting body-fixed radar. Island landmarks in the radar scans are tracked between multiple scans using visual features. This provides information not only about the position of the vessel but also of its course and velocity. We present here a complete navigation framework that requires no additional hardware than the already existing naval radar sensor. Experiments show that visual radar features can be used to accurately estimate the vessel trajectory over an extensive data set.

Place, publisher, year, edition, pages
Springer, 2011
Keyword
GPS, Navigation system, Radar, Sensor
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-72583 (URN)10.1186/1687-6180-2011-71 (DOI)000300999900001 ()
Funder
Swedish Foundation for Strategic Research Swedish Research Council
Note

funding agencies|Strategic Research Center MOVIII||Swedish Foundation for Strategic Research||SSF||CADICS||Swedish Research Council||

Available from: 2011-11-29 Created: 2011-11-29 Last updated: 2017-12-08Bibliographically approved
5. Silent Localization of Underwater Sensors Using Magnetometers
Open this publication in new window or tab >>Silent Localization of Underwater Sensors Using Magnetometers
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
Keyword
Underwater sensor localization, Sensor network, Magnetometers, SLAM
National Category
Signal Processing Control Engineering
Identifiers
urn:nbn:se:liu:diva-53589 (URN)10.1155/2010/709318 (DOI)000274966500001 ()
Projects
MOVIIICADICSLINK-SIC
Available from: 2010-01-25 Created: 2010-01-25 Last updated: 2017-12-12Bibliographically approved

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