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Abstract rincipal Component Analysis (PCA) has been widely used as a data reduction technique to overcome the curse of dimensionality. In this research a different use for PCA technique as a tool for data fusion is introduced. Principal component analysis method and fuzzy principal component analysis method have been described in this chapter to perform multisensor data fusion with a confidence measure associated with each sensor output. Although it was mentioned in chapter two, there is no perfect algorithm that is optimal under all conditions, table (6-7) below shows the simulation results comparison for all three approaches used to perform multi-sensor data fusion for the instant navigation system described in example 1 with minimum value of standard deviation for the error signal achieved by applying FAKF. Table (6-7). SIMULATION RESULTS COMPARISON 102 Sensory Data Fusion Based Fuzzy Principal Component Analysis .6 |