Dancing with Out-of-Sequence Data Measurements
Dr. Bhekisipho Twala (University of Johannesburg)
NICTA SML SEMINARDATE: 2011-03-17
TIME: 11:00:00 - 13:00:00
LOCATION: NICTA, 7 London Circuit, Board Room, Second floor
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ABSTRACT:
The issue of handling sensor measurements data over single and multiple lag delays is considered using model-based statistical imputation strategies for a multi-sensor tracking prediction problem. The effectiveness of two model-based imputation procedures against five out-of-sequence measurements (OOSM) methods is investigated using Monte Carlo simulation experiments. For single lag, estimates of target tracking computed from the observed data and those based on imputed data were equally unbiased; however, the Kalman filter (KF) estimates obtained using the Bayesian framework (BF-KF) were more precise. For multi-lag delayed measurements, there were significant differences in precision between multiple imputation (MI) and OOSM methods, with the former exhibiting a superior performance at nearly all levels of probability of measurement delay and range of manoeuvring indices.
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