Analog cellular networks for multisensor fusion and control

Analog modular architectures, derived from the computational paradigm of state-controlled cellular neural networks (SC-CNNs), are considered in this brief to process signals gathered from a distributed set of sensors. A novel design methodology for choosing the "local" system parameters so as to obtain the desired "global" signal processing function is proposed together with some theoretical results on sufficient conditions that guarantee asymptotic stability. An experimental prototype of cellular neural network for multisensor data fusion and control applications is presented and its adoption in the field of smart structures is discussed.