Multi-sensor systematic bias estimation method in ill-conditioned scenarios on the basis of ridge estimation

Multisensor bias estimation is a key precondition for the data fusion system to achieve performance superiority. Tratitional systematic bias estimation methods are numerically instable when applied into the ill-conditioned scenarios. Theoretical analysis is carried out on ill conditioning for two representative ill-conditioned scenarios, i.e., the tense-target scenario and the tense-sensor scenario. Then the systematic bias estimation method is proposed based on ridge estimation, which improves the numerical stability of the estimation results by relaxing the constraint of estimation unbiasedness. The approach of selecting the optimal ridge parameter is given under the constraint of the condition number. Simulation results demostrate that the propsed method is consistent with the lesat squares under good-conditioned scenarios, while it is superior to the traditional methods under tense-target scenarios. In the case of tense-sensor scenarios, the proposed method shows better performance on the range bias estimation. © 2017, Editorial Office of Systems Engineering and Electronics. All right reserved.