Max-Entropy Moment Filtering for Stochastic Hybrid Systems
For researchers working on filtering in stochastic hybrid systems, this provides a tractable method to handle non-Gaussian uncertainty without solving expensive PDEs.
The paper extends the Max-Entropy Moment Kalman Filter to stochastic hybrid systems, enabling filtering from partial statistical information by propagating moments through hybrid dynamics and reconstructing beliefs via maximum-entropy distributions. In a stochastic bouncing-ball example, the method captures reset-induced non-Gaussianity through corrected moment equations.
Stochastic hybrid systems combine continuous-time stochastic dynamics with discrete reset events, producing intrinsically non-Gaussian and often multimodal uncertainty. A consistent propagation law must also account for boundary-induced probability flux across guard sets, making direct density propagation through hybrid Fokker-Planck equations expensive. We develop a hybrid extension of the Max-Entropy Moment Kalman Filter (MEM-KF) that performs filtering from partial statistical information by propagating a finite collection of moments through stochastic hybrid dynamics and reconstructing beliefs using moment-constrained maximum-entropy distributions. The key step is a moment propagation rule derived from Dynkin's formula with a jump-sum, in which reset effects appear as a boundary-flux correction over the guard set. This yields tractable moment dynamics without solving the underlying hybrid PDE. In a stochastic bouncing-ball example, the proposed method captures reset-induced non-Gaussianity through corrected moment equations while retaining the MEM-KF's optimization-based maximum-entropy representation.