Please use this identifier to cite or link to this item: http://artemis.cslab.ece.ntua.gr:8080/jspui/handle/123456789/17648
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dc.contributor.authorKaparounakis, Orestis-
dc.date.accessioned2020-08-16T14:15:58Z-
dc.date.available2020-08-16T14:15:58Z-
dc.date.issued2020-07-21-
dc.identifier.urihttp://artemis.cslab.ece.ntua.gr:8080/jspui/handle/123456789/17648-
dc.description.abstractWe present a new method for automatically generating the implementation of state-estimation algorithms from a machine-readable description of the physical dynamics and signal constraints of sensing platforms. We implement the new state-estimator code generation method as a backend for a physics specification language and we apply the backend to generate complete C code implementations of state estimators for both linear systems (Kalman filters) and non-linear systems (extended Kalman filters). The state estimator code generation from physics specification is completely automated and requires no manual intervention. The generated filters can incorporate an automatic differentiation technique which combines function evaluation and differentiation in a single process. Using the description of physical system of a range of complexities, we generate extended Kalman filters, which we evaluate in terms of prediction accuracy using simulation traces. The results show that our automatically-generated sensor fusion and state estimation implementations provide state estimation within the same error bound as the human-written counterparts. We additionally quantify the code size and dynamic instruction count requirements of the generated state estimator implementations on the RISC-V architecture. The results show that our synthesized state estimation implementation employing automatic differentiation leads to an average improvement in the dynamic instruction count of the generated Kalman filter of 7%-16% compared to the standard differentiation technique. This is improvement comes at the limited cost of an average 4.5% increase in the code size of the generated filters.en_US
dc.languageenen_US
dc.subjectstate estimationen_US
dc.subjectcode generationen_US
dc.subjectKalman filtersen_US
dc.subjectcyber-physical systemsen_US
dc.subjectembedded systemsen_US
dc.subjectprogram synthesisen_US
dc.titleAutomated Code Generation for State Estimation Algorithmsen_US
dc.description.pages115en_US
dc.contributor.supervisorΣούντρης Δημήτριοςen_US
dc.departmentΤομέας Τεχνολογίας Πληροφορικής και Υπολογιστώνen_US
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