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/