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Stochastic observability test for discrete-time kalman filters
Vibhor L. Bageshwar
,
Demoz Gebre-Egziabher
, William L. Garrard
, Tryphon T. Georgiou
Aerospace Engineering and Mechanics
Electrical and Computer Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
49
Scopus citations
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Dive into the research topics of 'Stochastic observability test for discrete-time kalman filters'. Together they form a unique fingerprint.
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Engineering
Discrete Time
100%
Estimated State
14%
Extended Kalman Filter
14%
Illustrates
28%
Inertial navigation systems
14%
Initial State
14%
Kalman Filter
100%
Linear Time
14%
Observability
100%
Stochastic System
28%
Sufficient Condition
14%
System Matrix
14%
Keyphrases
Aided Inertial Navigation System
16%
Complete Uncertainty
16%
Discrete Kalman Filter
100%
Extended Kalman Filter
16%
Kalman Filter
33%
Linear Time Invariant
16%
Mean Vector
16%
Nonlinear Stochastic Systems
16%
State Vector
33%
Stochastic Observability
100%
Stochastic Time-varying Systems
16%
System Matrix
16%