Sensor fusion is a method of integrating signals from multiple sources. It allows extracting information from several different sources to integrate them into single signal or information. In many cases sources of information are sensors or other devices that allow for perception or measurement of changing environment. Information received from multiple-sensors is processed using "sensor fusion" or "data fusion" algorithms. These algorithms can be classified into three different groups. First, fusion based on probabilistic models, second, fusion based on least-squares techniques and third, intelligent fusion. The probabilistic model methods are Bayesian reasoning, evidence theory, robust statistics, recursive operators. The least-squares techniques are Kalman filtering, optimal theory, regularization and uncertainty ellipsoids. The intelligent fusion methods are fuzzy logic, neural networks and genetic algorithms. This paper will present three different methods of intelligent information fusion for different engineering applications. Chapter 2 is based on Sasiadek and Wang (2001) paper and presents an application of adaptive Kalman filtering to the problem of information fusion for guidance, navigation, and control. Chapter 3 is based on Sasiadek and Hartana (2000) and Chapter 4 on Sasiadek and Khe (2001) paper.
|Keywords||Fuzzy logic, Genetic algorithms, Least squares techniques, Neural networks, Probabilistic models, Sensor fusion|
|Journal||Annual Reviews in Control|
Sasiadek, J. (2002). Sensor fusion. Annual Reviews in Control, 26 II, 203–228. doi:10.1016/S1367-5788(02)00045-7