Optimal detection of bandlimited signals by multiple nonlinear sensor data fusion

A framework for the detection of bandlimited signals by optimally fusing the multi-nonlinear sensor data is developed. Though most sensors used are assumed to be linear, none of them individually or in series give 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. It is first shown that the optimal blend of multi-sensor data can be achieved by minimizing a weighted performance criterion. The result is then used to solve the sensor scheduling problem. The proposed theoretical framework is supported by illustrative examples and simulation data.