Optimal fusion of multiple nonlinear sensor data

A framework for the detection of bandlimited signals by optimally fusing the multinonlinear sensor data is developed. Though most sensors used are assumed to be linear, none of them individually or in series gives the truly linear relationship, and errors are inevitable as a result of the assumption of linearity. A new approach, which takes the actual nonlinear characteristics of sensors into account, is advocated. Though the fusion of redundant information can reduce the overall uncertainty and, thus, serves to increase the accuracy of the process measurements, identifying the faulty readings and fusing only the reliable data are very difficult and challenging. An optimal multiple nonlinear sensor data fusion scheme in which multisensor data fusion is done by scheduling the sensor measurements is proposed. The main idea of the multisensor fusion schemes proposed in this paper is to pick only the reliable data for the fusion and disregard the rest. The proposed theoretical framework is supported by illustrative examples and simulation data.