An Improved Algorithm for Universal Sensor Registration

We revise the ideas presented in a previous paper and propose an improved method for absolute sensor registration in target tracking applications. The approach uses targets of opportunity and, without making assumptions on their dynamical models, allows simultaneous calibration of multiple three- and two-dimensional sensors. The idea is representing the sensor angular misalignments as rotations of the actual position vectors by some rotation matrices. We formulate the registration task as a Maximum Likelihood (ML) estimation problem where the parameters to be estimated as the unknown rotation matrices as well as the unknown ground truth positions. Whereas for two-sensor scenarios only relative registration is possible, in practical cases with three or more sensors unambiguous absolute calibration may be achieved. The derived algorithm, as opposed to its previous version, is ensured to converge for three-dimensional scenarios. The derived algorithms are straightforward to implement and do not require tuning of parameters. The performance of the algorithms is tested in a numerical study.