Spectral Bayesian Estimation for General Stochastic Hybrid Systems

Abstract

General Stochastic Hybrid Systems (GSHS) have been formulated to represent various types of uncertainties in hybrid dynamical systems. In this paper, we propose computational techniques for Bayesian estimation of GSHS. In particular, the Fokker-Planck equation that describes the evolution of uncertainty distributions along GSHS is solved by spectral techniques, where an arbitrary form of probability density of the hybrid state is represented by a mixture of Fourier series. The method is based on splitting the Fokker-Planck equation represented by an integro-partial differential equation into the partial differentiation part for continuous diffusion and the integral part for discrete transition, and integrating the solution of each part. The propagated density function is used with a likelihood function in the Bayes' formula to estimate the hybrid state for given sensor measurements. The unique feature of the proposed technique is that the probability density describing complete stochastic properties of the hybrid state is constructed without relying on the common Gaussian assumption, in contrast to other methods that compute certain expected values such as mean or variance. We apply the proposed method to the estimation of two examples: the bouncing ball model and the Dubins vehicle model. We show that the proposed technique yields propagated densities consistent with Monte Carlo simulations, more accurate estimates over the Gaussian based approach and some computational benefits over a particle filter.

Uncertainty Propagation for General Stochastic Hybrid Systems on Compact Lie Groups

“…Alternatively, to propagate the probability density function directly, the FP equation has been extended for GSHS into integro-partial differential equations (IPDEs) [1,8]. And it has been solved using finite difference method [17] and spectral method [32].…”

“…In this regard, although the Monte Carlo method is also non-parametric, the information of the state is implicitly carried by random samples, which is usually hard to be distilled into usable forms other than calculating moments, especially when the number of samples is large. Also, the Monte Carlo method cannot deal with large uncertainties efficiently [32].…”