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Cooperative vehicle position estimation.

Parker, Ryan.

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Cooperative vehicle position estimation.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Cooperative vehicle position estimation./
作者:	Parker, Ryan.
面頁冊數:	89 p.
附註:	Source: Masters Abstracts International, Volume: 45-06, page: 3252.
Contained By:	Masters Abstracts International45-06.
標題:	Engineering, Automotive. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR27462
ISBN:	9780494274620

Cooperative vehicle position estimation.
Parker, Ryan.

Cooperative vehicle position estimation. - 89 p.

Source: Masters Abstracts International, Volume: 45-06, page: 3252.

Thesis (M.A.Sc.)--University of Toronto (Canada), 2007.

We present four vehicle localization (position estimation) algorithms. These algorithms can be broken into two classes: those which minimize the mean square error in the position estimates (i.e. the Kalman filter and the nonlinear least squares constrained algorithms), and those which minimize the maximum level of error in the position estimates (i.e. the Hinfinity filter and the constrained minimax algorithm). We show that gains in accuracy and reliability can be achieved over existing GPS-based approaches by making use of radio ranging based inter-vehicle distance measurements. Also, we show that the accuracy of previously proposed radio ranging based localization can be improved upon by taking into account additional information that is available to vehicles (e.g. digital road maps: vehicle kinematics). Then, after showing the benefits of using our algorithms, we present a detailed mathematical analysis and series of experiments that highlight the advantages and disadvantages of each algorithm.

ISBN: 9780494274620Subjects--Topical Terms:

1018477
Engineering, Automotive.

Cooperative vehicle position estimation.
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520 $a We present four vehicle localization (position estimation) algorithms. These algorithms can be broken into two classes: those which minimize the mean square error in the position estimates (i.e. the Kalman filter and the nonlinear least squares constrained algorithms), and those which minimize the maximum level of error in the position estimates (i.e. the Hinfinity filter and the constrained minimax algorithm). We show that gains in accuracy and reliability can be achieved over existing GPS-based approaches by making use of radio ranging based inter-vehicle distance measurements. Also, we show that the accuracy of previously proposed radio ranging based localization can be improved upon by taking into account additional information that is available to vehicles (e.g. digital road maps: vehicle kinematics). Then, after showing the benefits of using our algorithms, we present a detailed mathematical analysis and series of experiments that highlight the advantages and disadvantages of each algorithm.
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