1976 IEEE Conference on Decision and Control Including the 15th Symposium on Adaptive Processes 1976
DOI: 10.1109/cdc.1976.267794
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Kalman filter algorithms for a multi-sensor system

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Cited by 196 publications
(93 citation statements)
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“…The problem of finding optimal encoding algorithms for the multi-sensor case and analyzing their performance is similar to the problems of fusion of data from multiple sensors and of track-to-track fusion that have long been open. A usual starting point for the works that address these problems is an attempt to decentralize the Kalman filter as, e.g., in [43]. However this approach requires that data about the global estimate be sent from the fusion node to the local sensors.…”
Section: Introductionmentioning
confidence: 99%
“…The problem of finding optimal encoding algorithms for the multi-sensor case and analyzing their performance is similar to the problems of fusion of data from multiple sensors and of track-to-track fusion that have long been open. A usual starting point for the works that address these problems is an attempt to decentralize the Kalman filter as, e.g., in [43]. However this approach requires that data about the global estimate be sent from the fusion node to the local sensors.…”
Section: Introductionmentioning
confidence: 99%
“…[2]- [4]. Later on the Information filter, which is a nonhierarchical decentralized Kalman filter, is introduced where all sensors work in parallel to obtain local estimation based on their own and neighbors' information [5]- [9].…”
Section: Introductionmentioning
confidence: 99%
“…Other related publications cited in the Table 2.7 are [338]- [339], [337], [332], [333,334], [37], [46], [47], [48], [48], [60], [59,58], [57], [56], [55], [54], [53], [52]. Other related publications cited in the Table 2.8 are [67], [68], [91], [93,94], [95], [115], [116], [117], [124], [125], [127] and [128].…”
Section: Msdf Systemsmentioning
confidence: 99%