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PRODID:IEEE vTools.Events//EN
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DTSTART:20190331T030000
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DTSTART:20191027T020000
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BEGIN:VEVENT
DTSTAMP:20191208T180339Z
UID:09480E4E-8DD6-46FC-8364-8E98595380E1
DTSTART;TZID=Europe/Zurich:20190827T183000
DTEND;TZID=Europe/Zurich:20190827T203000
DESCRIPTION:The stochastic behavior of a navigation sensor is analyzed by c
 onstructing the Allan variance signatures from an error signal. For inerti
 al sensors\, such a signature is obtained by recording data at rest. The s
 ignal analysis results in suitable noise parameters. Nonetheless\, those p
 arameters may change under dynamic conditions. At first\, Dr. Clausen pres
 ents the influence of rotational dynamics on MEMS gyroscopes. Next\, he sh
 ows how to link this to the noise-parameter estimation in a rigorous way b
 y a modified version of the Generalized Method of Wavelet Moments (GMWM). 
 The results can then be used in a Kalman filter\, where the noise paramete
 rs are adapted according to such a predetermined functional relationship b
 etween sensor noise and the encountered dynamics of the platform/sensor.\n
 \nCo-sponsored by: Swiss Institute of Navigation ION-CH\n\nSpeaker(s): Dr.
  Philipp Clausen\, \n\nRoom: D 1.1\, Bldg: HG\, ETH\, Zurich\, Switzerland
 \, Switzerland
LOCATION:Room: D 1.1\, Bldg: HG\, ETH\, Zurich\, Switzerland\, Switzerland
ORGANIZER:philipp.clausen@ieee.org
SEQUENCE:1
SUMMARY:Construction of Dynamically-Dependent Stochastic Error Models
URL;VALUE=URI:https://events.vtools.ieee.org/m/213542
X-ALT-DESC:Description: <br /><p>The stochastic behavior of a navigation se
 nsor is analyzed by constructing the Allan variance signatures from an err
 or signal. For inertial sensors\, such a signature is obtained by recordin
 g data at rest. The signal analysis results in suitable noise parameters. 
 Nonetheless\, those parameters may change under dynamic conditions. At fir
 st\, Dr. Clausen presents the influence of rotational dynamics on MEMS gyr
 oscopes. Next\, he shows how to link this to the&nbsp\;noise-parameter est
 imation in a rigorous way by a modified version of the Generalized Method 
 of Wavelet Moments (GMWM). The results can then be used in a Kalman filter
 \, where the noise parameters are adapted according to such a predetermine
 d functional relationship between sensor noise and the encountered dynamic
 s of the platform/sensor.</p>
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