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- W2529366170 abstract "Since Cyber Physical Systems (CPS) are widely used inmany safety-critical domains these days, critical propertiessuch as robustness and resilience are required for such systems. To increase the robustness and the resilience of CPS, various sensor fusion techniques have been studied [1], [2],[3], [4]. These fusion techniques are based on certain sensormodels, which broadly fall into two categories: probabilisticmodel and abstract model. The probabilistic sensor model [1] uses certain noisedistributions on sensors (e.g., Gaussian), which is wellsuited for analyzing the systems' expected performance inthe average case. However, wrong assumptions on noisedistributions may be in danger of being vulnerable to sensorattacks. On the other hand, the abstract sensor model [2] usesthe worst-case error bound of sensors. Thus, this model iswell suited for the systems' worst-case performance, whichis highly relevant to the case of sensor attacks [3].In this work, we study a hybrid sensor fusion that usesboth probabilistic and abstract sensor models to be able tobenefit from both. We demonstrate the validation of ourhybrid sensor fusion technique using an unmanned groundvehicle called Jackal." @default.
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- W2529366170 date "2016-08-01" @default.
- W2529366170 modified "2023-09-25" @default.
- W2529366170 title "Toward a Hybrid Sensor Fusion Using Probabilistic and Abstract Sensor Models" @default.
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- W2529366170 doi "https://doi.org/10.1109/rtcsa.2016.33" @default.
- W2529366170 hasPublicationYear "2016" @default.
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