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Title: Tracking and estimation algorithms for bearings only measurements
Author: Ozelci, Attila Can
ISNI:       0000 0004 2732 4136
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2013
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The Bearings-only tracking problem is to estimate the state of a moving object from noisy observations of its direction relative to a sensor. The Kalman filter, which provides least squares estimates for linear Gaussian filtering problems is not directly applicable because of the highly nonlinear measurement function of the state, representing the bearings measurements and so other types of filters must be considered. The shifted Rayleigh filter (SRF) is a highly effective moment-matching bearings-only tracking algorithm which has been shown, in 2D, to achieve the accuracy of computationally demanding particle filters in situations where the well-known extended Kalman filter and unscented Kalman filter often fail. This thesis has two principal aims. The first is to develop accurate and computationally efficient algorithms for bearings-only tracking in 3D space. We propose algorithms based on the SRF, that allow tracking, in the presence of clutter, of both nonmaneuvering and maneuvering targets. Their performances are assessed, in relation to competing methods, in highly challenging tracking scenarios, where they are shown to match the accuracy of high-order sophisticated particle filters, at a fraction of the computational cost. The second is to design accurate and consistent algorithms for bearings-only simultaneous localization and mapping (SLAM). The difficulty of this problem, originating from the uncertainty in the position and orientation of the sensor, and the absence of range information of observed landmarks, motivates the use of advanced bearings-only tracking algorithms. We propose the quadrature-SRF SLAM algorithm, which is a moment-matching filter based on the SRF, that numerically evaluates the exact mean and covariance of the posterior. Simulations illustrate the accuracy and consistency of its estimates in a situation where a widely used moment-matching algorithm fails to produce consistent estimates. We also propose a Rao-Blackwellized SRF implementation of a particle filter, which, however, does not exhibit favorable consistency properties.
Supervisor: Vinter, Richard Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral