Accession Number ADA562462
Title Decentralized Riemannian Particle Filtering with Applications to Multi- Agent Localization.
Publication Date Jun 2012
Media Count 211p
Personal Author M. J. Eilders
Abstract The primary focus of this research is to develop consistent nonlinear decentralized particle filtering approaches to the problem of multiple agent localization. A key aspect in our development is the use of Riemannian geometry to exploit the inherently non-Euclidean characteristics that are typical when considering multiple agent localization scenarios. A decentralized formulation is considered due to the practical advantages it provides over centralized fusion architectures. Inspiration is taken from the relatively new field of information geometry and the more established research field of computer vision. Differential geometric tools such as manifolds, geodesics, tangent spaces, exponential, and logarithmic mappings are used extensively to describe probabilistic quantities. Numerous probabilistic parameterizations were identified, settling on the efficient square-root probability density function parameterization. The square-root parameterization has the benefit of allowing filter calculations to be carried out on the well studied Riemannian unit hypersphere. A key advantage for selecting the unit hypersphere is that it permits closed-form calculations, a characteristic that is not shared by current solution approaches. Through the use of the Riemannian geometry of the unit hypersphere, we are able to demonstrate the ability to produce estimates that are not overly optimistic. Results are presented that clearly show the ability of the proposed approaches to outperform current state- of-the-art decentralized particle filtering methods. In particular, results are presented that emphasize the achievable improvement in estimation error, estimator consistency, and required computation al burden.
Keywords Data fusion
Decentralization
Differential geometry
Localization
Mathematical filters
Multiagent systems
Particle filtering
Position(Location)
Riemannian geometry
Theses


 
Source Agency Non Paid ADAS
NTIS Subject Category 72B - Algebra, Analysis, Geometry, & Mathematical Logic
72F - Statistical Analysis
62 - Computers, Control & Information Theory
Corporate Author Air Force Inst. of Tech., Wright-Patterson AFB, OH. School of Engineering and Management.
Document Type Thesis
Title Note Doctoral thesis.
NTIS Issue Number 1225
Contract Number N/A

Science and Technology Highlights

See a sampling of the latest scientific, technical and engineering information from NTIS in the NTIS Technical Reports Newsletter

Acrobat Reader Mobile    Acrobat Reader