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UID:0-509@aerospace.technion.ac.il

DTSTART;TZID=Asia/Jerusalem:20150708T163000

DTEND;TZID=Asia/Jerusalem:20150708T173000

DTSTAMP:20230603T192545Z

URL:https://aerospace.technion.ac.il/events/the-2015-merhav-prize-seminar-
 state-estimation-in-systems-with-random-coefficients/

SUMMARY:The 2015 Merhav Prize seminar - State Estimation in Systems with Ra
 ndom Coefficients
DESCRIPTION:Lecturer:Daniel Sigalov\n Faculty:\n Institute:Technion – Isr
 ael Institute of Technology\n Location:Classroom 165\, ground floor\, Libr
 ary\, Aerospace Eng.\n Zoom: \n Abstract: \n Details: \n State estimation 
 in dynamical systems with randomly switching coefficients is an important
  problem with a variety of applications. The most commonly met problems t
 hat are formulated within the random coefficients state-space approach\, 
 are maneuvering target tracking and fault-tolerant filtering. A related cl
 ass of problems is referred to as data association or data ambiguity. In 
 this family\, the estimation process is further complicated by the fact th
 at the acquired data has uncertain origin. Typical applications include t
 arget tracking in clutter and multiple target tracking.  In this talk we
  aim at deriving a unified framework for state estimation problems under 
 data and model uncertainties. First\, we present the problem of tracking a
  maneuvering target restricted to perform a bounded number of maneuvers a
 nd show that\, despite the inherent deviation from the standard Markov sw
 itching assumption underlying most state-of-the-art algorithms\, it may b
 e reformulated and solved using a generalized version of such tools. We p
 roceed with an overview of linear minimum mean-square error (LMMSE) algor
 ithms for state estimation in systems with random coefficients. We show th
 at the novel formulation allows treatment of problems that have not been 
 addressed in the LMMSE sense in the past. These include target tracking i
 n clutter and multiple target tracking. Finally\, we present a general fr
 amework\, accompanied by a variety of applications\, which allows a utiliz
 ation of a single IMM-like algorithm to solve a variety of state estimatio
 n problems.  These include maneuvering target tracking\, clutter and data
  association\, multiple target tracking\, tracking of splitting targets an
 d more. In some cases it will be shown that the resulting unified IMM-lik
 e filter reduces to some classical algorithms such as PDA and JPDA.
CATEGORIES:Seminars
LOCATION:Classroom 165\, ground floor\, Library\, Aerospace Eng.

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