Sensor fusion by principal and independent component decomposition using neural networks

Abstract

The paper describes a view to use both principal component analysis (PCA) and independent component analysis (ICA) within the context of sensor fusion. A nonlinear version of PCA would be appropriate for representing signals/data which span a submanifold structure in its coordinate space. The nonlinear PCA is a candidate for data reduction/compression where multisensors are measurement the same type of signal, e.g., image or sound. In contrast the ICA is a candidate for fusing different types of signals, e.g., image, sound, acceleration, etc., to generate independent components. The PCA approach can be used to transfer compressed data then reconstruct the information bearing signal for use. While the ICA may be used to infer the condition/state of the environment, e.g., office building, airport, etc. Thus the two approaches can be integrated to form a complementary sensory fusion system.