Simultaneous tracking and sparse calibration in ground sensor networks using evidence approximation

Calibration of ground sensor networks is a complex task in practice. To tackle the problem, we propose an approach based on simultaneous tracking of targets of opportunity and sparse estimation of the bias parameters. The evidence approximation method is used to get a sparse estimate of the bias parameters, and the method is here extended with a novel marginalization step where a state smoother is invoked. A simulation study shows that the non-zero bias parameters are detected and well estimated using only one target of opportunity passing by the network.