An expectation-maximization-based approach to the relative grid-locking problem

An important prerequisite for successful multisensory integration is that the data from the reporting sensors are transformed to a common reference frame free of systematic or registration bias errors. If not properly corrected, the registration errors can seriously degrade the global surveillance system performance. The relative sensor registration (or grid-locking) process aligns remote data to local data under the assumption that the local data are bias free and that all biases reside with the remote sensor. In this paper, we take into account all registration errors involved in the grid-locking problem. An EM-based estimator of these bias terms is derived and its statistical performance compared to the hybrid Cramér-Rao lower bound (HCRLB).