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Bayesian Surface and Underwater Navigation
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.
2006 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 54, no 11, 4204-4213 p.Article in journal (Refereed) Published
Abstract [en]

A common framework for maritime surface and underwater (UW) map-aided navigation is proposed as a supplement to satellite navigation based on the global positioning system (GPS). The proposed Bayesian navigation method is based on information from a distance measuring equipment (DME) which is compared with the information obtained from various databases. As a solution to the recursive Bayesian navigation problem, the particle filter is proposed. For the described system, the fundamental navigation performance expressed as the Crameacuter-Rao lower bound (CRLB) is analyzed and an analytic solution as a function of the position is derived. Two detailed examples of different navigation applications are discussed: surface navigation using a radar sensor and a digital sea chart and UW navigation using a sonar sensor and a depth database. In extensive Monte Carlo simulations, the performance is shown to be close to the CRLB. The estimation performance for the surface navigation application is in comparison with usual GPS performance. Experimental data are also successfully applied to the UW application.

Place, publisher, year, edition, pages
2006. Vol. 54, no 11, 4204-4213 p.
Keyword [en]
Cramér-Rao lower bound, Particle filter, Recursive Bayesian estimation, Sea navigation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-50088DOI: 10.1109/TSP.2006.881176OAI: oai:DiVA.org:liu-50088DiVA: diva2:270984
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-12
In thesis
1. Particle filtering for positioning and tracking applications
Open this publication in new window or tab >>Particle filtering for positioning and tracking applications
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A Bayesian approach to positioning and tracking applications naturally leads to a recursive estimation formulation. The recently invented particle filter provides a numerical solution to the non-tractable recursive Bayesian estimation problem. As an alternative, traditional methods such as the extended Kalman filter. which is based on a linearized model and an assumption on Gaussian noise, yield approximate solutions.

In many practical applications, signal quantization and algorithmic complexity are fundamental issues. For measurement quantization, estimation performance is analyzed in detail. The algorithmic complexity is addressed for the marginalized particle filter, where the Kalman filter solves a linear subsystem subject to Gaussian noise efficiently.

The particle filter is adopted to several positioning and tracking applications and compared to traditional approaches. Particularly, the use of external database information to enhance estimation performance is discussed. In parallel, fundamental limits are derived analytically or numerically using the Cramér-Rao lower bound, and the result from estimation studies is compared to the corresponding lower bound. A framework for map-aided positioning at sea is developed, featuring an underwater positioning system using depth information and readings from a sonar sensor and a novel surface navigation system using radar measurements and sea chart information. Bayesian estimation techniques are also used to improve position accuracy for an industrial robot. The bearings-only tracking problem is addressed using Bayesian techniques and map information is used to improve the estimation performance. For multiple-target tracking problems data association is an important issue. A method to incorporate classical association methods when the estimation is based on the particle filter is presented. A real-time implementation of the particle filter as well as hypothesis testing is introduced for a collision avoidance application.

Place, publisher, year, edition, pages
Linköping, Sweden: Linköping University Electronic Press, 2005. 55 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 924
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-29608 (URN)14987 (Local ID)91-85297-34-8 (ISBN)14987 (Archive number)14987 (OAI)
Public defence
2005-03-18, Sal Visionen, Campus Valla, Linköping, 10:15 (Swedish)
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2012-11-29Bibliographically approved

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Karlsson, RickardGustafsson, Fredrik

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