Non-linear filtering for a dust-perturbed two-body model

Abstract

In standard textbooks on classical mechanics, the two-body central forcing problem is formulated as a system of the coupled non-linear second-order deterministic differential equations. Uncertainties, introduced by the astronomical ‘dust’, are not assumed in the orbit dynamics. The dust population produces an additional random force on the orbiting particle. This work is a continuation of the paper (Sharma and Parthasarathy, Proc. R. Soc. A: Math. Phys. Eng. Sci. 463:979–1003, [2007]) in which the authors developed and analyzed the dust-perturbed two-body model, which accounts for the dust perturbation felt by the orbiting particle. The theory of the dust-perturbed stochastic system was developed using the Fokker–Planck equation. This paper discusses the problem of realizing non-linear stochastic filters for estimating the states of the dust-perturbed planar two-body stochastic system, especially from noisy observations. This paper utilizes the Kushner’s theory of non-linear filtering, which involves stochastic observation term in the evolution of conditional probability density, for deriving the stochastic evolutions of the conditional mean and conditional covariance. The effectiveness of the non-linear filters of this paper is examined on the basis of their ability to preserve the perturbation effect, less random fluctuations in the mean trajectory and stability characteristics in the mean and variance trajectories. Most notably, this paper reveals the efficacy of the second-order approximate Kushner filter for the estimation procedure in contrast to the first-order approximate filter. Simulation results are introduced to demonstrate the usefulness of an analytic theory developed in this paper.