IMPUTATION OF MISSING DATA USING BAYESIAN PRINCIPAL COMPONENT ANALYSIS ON TEC IONOSPHERIC SATELLITE DATASET

The ionosphere is defined as a region of the earth's upper atmosphere where sufficient ionisation can exist to affect the propagation of radio waves. Estimation of missing data of ionosphere total electron content (TEC) are crucial and remain a challenge for GPS positioning and navigation system, space weather forecast, as well as many other Earth Observation System. There are a number of alternate ways of dealing with missing data, and this research work is an attempt at BPCA (Bayesian Principal Component Analysis). The work also focuses on comparison with k-nearest neighbor and error rate is being measured accordingly. The evaluation is carried out in satellite dataset which is used to predict total electron content in ionosphere. From the experimental results, it becomes evident that BPCA could well estimate the missing data and converge good short term performance on the taken dataset.