| || Handling Out-of-Sequence Data: Kalman Filter Methods or Statistical Imputation?
Author : Twala, Bhekisipho
Source : Defence Science Journal ; Vol:60(1) ; 2010 ; pp 087-099
Subject : 681.3 Computer Science;Defence Science Journal
Keywords : Multi-Sensor Data;Time Delayed Measurements;Out-of-Sequence Measurements;Kalman Filter;Fusion;Imputation;Multi-Sensor Tracking
Abstract : The issue of handling sensor measurements data over single and multiple lag delays also known as outof- sequence measurement (OOSM) has been considered. It is argued that this problem can also be addressed using model-based imputation strategies and their application in comparison to Kalman filter (KF)-based approaches for a multi-sensor tracking prediction problem has also been demonstrated. The effectiveness of two model-based imputation procedures against five OOSM methods was investigated in Monte Carlo simulation experiments. The delayed measurements were either incorporated (or fused) at the time these were finally available (using OOSM methods) or imputed in a random way with higher probability of delays for multiple lags and lower probability of delays for a single lag (using single or multiple imputation). For single lag, estimates of target tracking computed from the observed data and those based on a data set in which the delayed measurements were imputed were equally unbiased; however, the KF estimates obtained using the Bayesian framework (BF-KF) were more precise. When the measurements were delayed in a multiple lag fashion, there were significant differences in bias or 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. Researchers working on sensor data are encouraged to take advantage of software to implement delayed measurements using MI, as estimates of tracking are more precise and less biased in the presence of delayed multi-sensor data than those derived from an observed data analysis approach.