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UID:0-232@aerospace.technion.ac.il

DTSTART;TZID=Asia/Jerusalem:20210630T163000

DTEND;TZID=Asia/Jerusalem:20210630T173000

DTSTAMP:20230409T143424Z

URL:https://aerospace.technion.ac.il/events/an-improved-distributed-consen
 sus-kalman-filter-design-approach/

SUMMARY:An Improved Distributed Consensus Kalman Filter Design Approach
DESCRIPTION:Lecturer:Aviv Priel\n Faculty:Department of Aerospace Engineeri
 ng\n Institute:Technion – Israel Institute of Technology\n Location:Clas
 sroom 165\, ground floor\, Library\, Aerospace Eng.\n Zoom: \n Abstract: \
 n Details: \n Sensor networks comprise a group of agents equipped with sen
 sing devices and communicating capabilities in order to solve the common t
 ask of cooperative estimation of a detectable physical process. In this fr
 amework\, each agent in the system activates\, in a distributed fashion\, 
 an estimator\, which relies on local measurements fused with the estimates
  from other agents in the network.\nA recently developed tool to solve thi
 s problem is the introduction of a consensus-based term fused with a class
 ical state estimator structure\, knows as the consensus Kalman filter. In 
 this architecture\, each estimator incorporates a classic Kalman term\, al
 ong with a consensus term used to fuse the estimates from neighboring agen
 ts. Our contribution begins with proposing a method based on semi-definite
  programming to compute a centralized consensus gain term leading to impro
 ved performance of the estimator over existing solutions.\nWe also propose
  a decentralized consensus gain that can be computed by each agent and rel
 ies only on local network properties.\nWe further extend our research to t
 ackle the important aspects of reducing energy (communication) consumption
  in network applications. To do so\, we utilize an event triggering mechan
 ism in which communication is permitted only if certain conditions are met
 . The main analytical challenge in these  estimator structures is the des
 ign of the consensus gain term and an event trigger condition that ensures
  stability while maintaining a prescribed degree of performance. In this d
 irection\, our contribution continues with proposing both a centralized an
 d a decentralized consensus gain along with a tailored event triggered con
 dition. We show that these event trigger estimators out-performs the stand
 ard non-cooperative local Kalman filter. We provide numerical simulations 
 to demonstrate the effectiveness of our results compared to existing solut
 ions in the literature.
CATEGORIES:Seminars
LOCATION:Classroom 165\, ground floor\, Library\, Aerospace Eng.

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