BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//talks.cam.ac.uk//v3//EN
BEGIN:VTIMEZONE
TZID:Europe/London
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:19700329T010000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:19701025T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
CATEGORIES:CUED Control Group Seminars
SUMMARY:Sensor fusion and parameter inference in nonlinear
dynamical systems - Thomas B. Schön\, Associate
Professor\, Linköping University
DTSTART;TZID=Europe/London:20130418T110000
DTEND;TZID=Europe/London:20130418T123000
UID:TALK44505AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/44505
DESCRIPTION:In this talk I will provide an overview of some of
the work we do when it comes to solving inference
\nproblems in nonlinear dynamical systems. A prope
r subtitle for the talk is "strategies and\nexampl
es"\, since I will only provide solution strategie
s and show that these strategies can\nsuccessfully
solve challenging applications. The first topic i
s parameter inference problems in\nnonlinear dynam
ical systems (a.k.a. nonlinear system identificati
on). The maximum likelihood problem\nis solved usi
ng a combination of the expectation maximization (
EM) algorithm and sequential Monte\nCarlo (SMC) me
thods (e.g.\, the particle filter and the particle
smoother). The Bayesian problem is\nsolved using
a combination of Markov chain Monte Carlo (MCMC) a
nd SMC. As an example\, we show how to\nestimate a
particular special case known as the Wiener model
(a linear dynamical model followed by a\nstatic n
onlinearity). The second topic is that of sensor f
usion\, which refers to the problem of\ninferring
states (and possibly parameters) using measurement
s from several different\, often\ncomplementary\,
sensors. The strategy is explained and (perhaps mo
re importantly) illustrated using\nthree of the in
dustrial applications we are working with\; 1. Nav
igation of fighter aircraft (using\ninertial senso
rs\, radar and maps)\; 2. Indoor positioning of hu
mans (using inertial sensors and\nmaps)\; 3. Indoo
r pose estimation of a human body (using inertial
sensors and ultra-wideband).
LOCATION:Cambridge University Engineering Department\, LR3A
CONTACT:Dr Jason Z JIANG
END:VEVENT
END:VCALENDAR