Cramér-Rao type lower bounds for relative sensor registration process

This paper concerns the study of the Cramér-Rao type lower bounds for relative sensor registration (or grid-locking) problem. The theoretical performance bound is of fundamental importance both for algorithm performance assessment and for prediction of the best achievable performance given sensor locations, sensor number, and accuracy of sensor measurements. First, a general description of the relative grid-locking problem is given. Afterwards, the measurement model is analyzed. In particular, the nonlinearity of the measurement model and all the biases (attitude biases, measurement biases, and position biases) are taken into account. Finally, the Cramér-Rao lower bound (CRLB) is discussed and two different types of CRLB, the Hybrid CRLB (HCRLB) and the Modified CRLB (MCRLB), are calculated. Theoretical and simulated results are shown.

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