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We present a new method for automatically generating the implementation ofstate-estimation algorithms from a machine-readable specification of thephysics of a sensing system and physics of its signals and signal constraints.We implement the new state-estimator code generation method as a backend for aphysics specification language and we apply the backend to generate complete Ccode implementations of state estimators for both linear systems (Kalmanfilters) and non-linear systems (extended Kalman filters). The state estimatorcode generation from physics specification is completely automated and requiresno manual intervention. The generated filters can incorporate an AutomaticDifferentiation technique which combines function evaluation anddifferentiation in a single process. Using the description of physical systemof a range of complexities, we generate extended Kalman filters, which weevaluate in terms of prediction accuracy using simulation traces. The resultsshow that our automatically-generated sensor fusion and state estimationimplementations provide state estimation within the same error bound as thehuman-written hand-optimized counterparts. We additionally quantify the codesize and dynamic instruction count requirements of the generated stateestimator implementations on the RISC-V architecture. The results show that oursynthesized state estimation implementation employing Automatic Differentiationleads to an average improvement in the dynamic instruction count of thegenerated Kalman filter of 7%-16% compared to the standard differentiationtechnique. This is improvement comes at the limited cost of an average 4.5%increase in the code size of the generated filters.

Automated Physics-Derived Code Generation for Sensor Fusion and State Estimation