Information processing has always been an important factor in the development of human society, and its role is still increasing. The inventions of advanced information devices paved the way for achievements in a diversity of fields like trade, navigation, agriculture, industry, transportation, and communication. The term ‘information device’ refers here to systems for the sensing, acquisition, processing, and outputting of information from the real world. Usually, they are measurement systems. Sensing and acquisition provide us with signals that bear a direct relation to some of the physical properties of the sensed object or process. Often, the information of interest is hidden in these signals. Further signal processing is needed to reveal the information, and to transform it into an explicit form.

The three topics discussed in this book, classification, parameter estimation, and state estimation, share a common factor in the sense that each topic provides the theory and methodology for the functional design of the signal processing part of an information device. The major distinction between the topics is the type of information that is out-putted. In classification problems the output is discrete, i.e. a class, a label, or a category. In estimation problems, it is a real-valued scalar or vector. Since these problems occur either in a static or in a dynamic setting, actually four different topics can be distinguished. The term state estimation refers to the dynamic ...

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