LEADER 00000nam 2200313 4500
001 AAI3292415
005 20081215124319.5
008 081215s2008 ||||||||||||||||| ||eng d
020 9780549357070
035 (UMI)AAI3292415
040 UMI|cUMI
100 1 Schumitsch, Brad
245 10 Large scale tracking with data association ambiguity: The
identity-management Kalman filter
300 79 p
500 Source: Dissertation Abstracts International, Volume: 68-
12, Section: B, page: 8277
500 Adviser: Kunle Olukotun
502 Thesis (Ph.D.)--Stanford University, 2008
520 Tracking objects from measurements is an important problem
with wide applications from security, to the safety of
small children, to identifying cars on public streets to
assist in autonomous driving. If the correspondence
between measurements and objects is known, the well-known
Kalman filter can be used. However, in many real-world
problems this correspondence is not known and we are
forced to confront what is known as the data association
problem. It is believed the best exact solution is
exponential (the problem is P# complete), so current
research is focused on approximations
520 In this thesis, we introduce an on-line approximate filter
that empirically performs very well on large-scale
problems. This filter tightly integrates the continuous
aspects of the problem (locating the objects) with the
discrete aspects (the data association ambiguities). This
filter contains a way to compactly, albeit approximately,
represent exponentially many hypotheses. The
representation involves an N x N matrix, which represents
a distribution over N! hypotheses for the location of the
objects. We derive the update of this filter show
experimental results that our filter outperforms state-of-
the-art approaches
590 School code: 0212
590 DDC
650 4 Engineering, Electronics and Electrical
690 0544
710 2 Stanford University
773 0 |tDissertation Abstracts International|g68-12B
856 40 |uhttp://pqdd.sinica.edu.tw/twdaoapp/servlet/
advanced?query=3292415
