Abstract
We consider a challenging scenario in this research, where the sensors may receive spurious sensor data, potentially causing inconsistent state estimates. Covariance union (CU) is a fault-tolerant algorithm that can deal with inconsistent state estimation fusion. However, existing CU algorithms suffer from high computational costs due to optimizing nonlinear cost functions when generating fusion weights. To overcome this deficiency, an efficient CU algorithm named fast covariance union (FCU) is developed. We have proved that the fusion weight of FCU can be optimally generated by a closed-form algorithm without optimizing any nonlinear cost function, leading to better fusion efficiency. In addition, the FCU algorithm ensures the fused estimate be consistent as long as one of the estimates is consistent. Finally, the Monte Carlo simulation results show that the FCU algorithm has higher computational efficiency than the existing CU algorithms and handles the spurious sensor data fusion effectively.
Highlights
M ULTISENSOR fusion is an effective way to improve the reliability, robustness, and accuracy of the estimate system by combining information from different individual sensors
We have presented an fast covariance union (FCU) algorithm to solve this problem
We have proved that the fusion weight of FCU can be optimally generated by a closed-form algorithm without optimizing any nonlinear cost function
Summary
M ULTISENSOR fusion is an effective way to improve the reliability, robustness, and accuracy of the estimate system by combining information from different individual sensors. Depending on whether the raw data are processed, the architecture of existing multisensor systems can be classified into two groups: centralized and distributed In the former, raw data are directly sent to a central node and are fused for state estimates, leading to optimal global estimates. Covariance intersection (CI) [15] is a well-known consistent fusion approach, which can obtain consistent estimates even facing an unknown degree of inter-estimate correlation It has been widely used in many fields [12], [16]–[18]. A fast covariance union (FCU) algorithm is proposed to solve the multisensor fusion problem with spurious sensor data. 3) The FCU algorithm ensures the fused estimate be consistent as long as one of the estimates is consistent
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