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UID:0-331@aerospace.technion.ac.il

DTSTART;TZID=Asia/Jerusalem:20190617T163000

DTEND;TZID=Asia/Jerusalem:20190617T173000

DTSTAMP:20230527T132521Z

URL:https://aerospace.technion.ac.il/events/the-2019-merhav-prize-seminar-
 estimation-based-guidance-using-optimal-bayesian-decision/

SUMMARY:The 2019 Seminar in Memory of Prof. Shmuel and Mrs. Noemi Merhav - 
 Estimation-Based Guidance Using Optimal Bayesian Decision
DESCRIPTION:Lecturer:Liraz Mudrik\n Faculty:Faculty of Aerospace Engineerin
 g\n Institute:Technion – Israel Institute of Technology\n Location:Class
 room 165\, ground floor\, Library\, Aerospace Eng.\n Zoom: \n Abstract: \n
  Details: \n Derived based on an idealized\, perfect information different
 ial game\, the acclaimed DGL1 guidance law was shown to guarantee hit-to-k
 ill performance in the linearized\, deterministic case\, involving an inte
 rceptor possessing superior maneuverability and agility. In real life scen
 arios\, where the perfect information assumption never holds\, this advanc
 ed guidance law has to be augmented with a separately designed estimator t
 hat provides an estimate of the missing information. However\, the inheren
 t estimation error leads to erroneous decisions on the part of the guidanc
 e law\, resulting in a severe interception performance degradation.\nTo al
 leviate this performance degradation\, we use Bayesian decision theory to 
 make optimal decisions on the game’s state\, that properly take into acc
 ount the inherent uncertainty due to the estimation error\, on the one han
 d\, while also considering the cost (final miss distance) associated with 
 making various decisions on the game’s state\, on the other hand. In tur
 n\, we modify the DGL1 law to use these optimal decisions\, which results 
 in a new\, estimation-aware guidance law.\nThe implementation of the Bayes
 ian decision rule requires the knowledge of the game’s posterior probabi
 lity density function. To provide this density we employ an interacting mu
 ltiple model particle filter\, which is capable of dealing with nonlinear\
 , non-Gaussian and even non-Markovian mode switching problems. The target 
 acceleration command mode is modeled as a non-homogeneous Markov model\, t
 o cope with sophisticated targets that optimally time their evasion maneuv
 ers. The performance of the new guidance law is demonstrated via an extens
 ive Monte-Carlo simulation study\, where it is compared with the classical
  DGL1.
CATEGORIES:Seminars
LOCATION:Classroom 165\, ground floor\, Library\, Aerospace Eng.

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