Tolerating failures of continuous-valued sensors
ACM Transactions on Computer Systems (TOCS)
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Multisensor Decision and Estimation Fusion
Multisensor Decision and Estimation Fusion
How to reconcile fault-tolerant interval intersection with the Lipschitz condition
Distributed Computing
Technical Communique: The optimality for the distributed Kalman filtering fusion with feedback
Automatica (Journal of IFAC)
Optimal linear estimation fusion .I. Unified fusion rules
IEEE Transactions on Information Theory
Imprecise expectations for imprecise linear filtering
International Journal of Approximate Reasoning
Use of the domination property for interval valued digital signal processing
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
| Hi-index | 22.14 |
The interval estimation fusion method based on sensor interval estimates and their confidence degrees is developed. When sensor estimates are independent of each other, a combination rule to merge sensor estimates and their confidence degrees is proposed. Moreover, two optimization criteria: minimizing interval length with an allowable minimum confidence degree, or maximizing confidence degree with an allowable maximum interval length are suggested. In terms of the two criteria, an optimal interval estimation fusion can be obtained based on the combined intervals and their confidence degrees. Then we can extend the results on the combined interval outputs and their confidence degrees to obtain a conditional combination rule and the corresponding optimal fault-tolerant interval estimation fusion in terms of the two criteria. It is easy to see that Marzullo's fault-tolerant interval estimation fusion [Marzullo, (1990). Tolerating failures of continuous-valued sensors. ACM Transactions on Computer System, 8(4), 284-304] is a special case of our method.