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UID:0-227@aerospace.technion.ac.il

DTSTART;TZID=Asia/Jerusalem:20211108T163000

DTEND;TZID=Asia/Jerusalem:20211108T173000

DTSTAMP:20230409T142716Z

URL:https://aerospace.technion.ac.il/events/stochastic-interception-using-
 particle-filtering-and-smoothing-and-a-time-varying-delayed-information-ga
 me/

SUMMARY:Stochastic Interception Using Particle Filtering and Smoothing\, an
 d A Time-Varying Delayed-Information Game
DESCRIPTION:Lecturer:Liraz Mudrik\n Faculty:Department of Aerospace Enginee
 ring\n Institute:Technion – Israel Institute of Technology\n Location:Cl
 assroom 165\, ground floor\, Library\, Aerospace Eng. & https://technion.z
 oom.us/j/99045396257\n Zoom: \n Abstract: \n Details: \n In a real-world\,
  stochastic pursuit-evasion game\, a sudden change in the target's evasion
  maneuver generates an inevitable time delay at the pursuer's estimator\, 
 due to its inherent reliance on incomplete and noisy measurements. In turn
 \, this time delay might severely degrade the pursuer's worst-case interce
 ption performance. A common approach to address this challenge is to incor
 porate time delay(s) in the deterministic derivation of the guidance law. 
 However\, these solutions unrealistically assume that the delay is constan
 t throughout the game\, and\, because the value of the delay is unknown\, 
 it is generally regarded as a tuning parameter only. Moreover\, these solu
 tions feed the delayed-information guidance law with current (filtered) ti
 me estimates\, thus breaking the assumptions underlying the derivation of 
 this law.\nWe present a novel tracking and interception strategy that addr
 esses the aforementioned problems in a game involving highly maneuverable 
 targets. We first solve a bounded-control differential game with two time-
 varying delays\, thus extending an already known result. Second\, we intro
 duce a novel algorithm for online estimation of these delays using semi-Ma
 rkov chains\, without requiring the standard assumptions of linearity and 
 Gaussian distributions. Finally\, as the developed guidance law requires d
 elayed information about two states\, we use a fixed-lag particle smoother
  to provide the best information at the appropriate time.  This approach 
 renders an improved framework for intercepting an evasively maneuvering ta
 rget in a stochastic setting\, where a sophisticated target can exploit th
 e inherent estimation delay for evasion.  We evaluate the performance of 
 this methodology using a thorough Monte-Carlo simulation study.\nZoom Meet
 ing
CATEGORIES:Seminars
LOCATION:Classroom 165\, ground floor\, Library\, Aerospace Eng. & https://
 technion.zoom.us/j/99045396257

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