Handling out-of-sequence data using model-based statistical imputation

The issue of handling sensor measurement data over single and multiple lag delays is considered using model-based imputation strategies for a multi-sensor tracking prediction problem. The effectiveness of two model-based imputation procedures against five out-of-sequence measurement (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 and OOSM methods, with the former exhibiting a superior performance at nearly all levels of probability of measurement delay and range of manoeuvring indices.