Jose Antonio Rebollo

Jose Antonio Rebollo, Enrico Barbiero, Marco Lovera

In this paper the problem of continuous-time subspace identification for Linear Time Invariant (LTI) systems is considered and a method which directly identifies a continuous-time state-space form is proposed. First, Hermite basis functions are used to project signals and obtain a finite number of Hermite coefficients. By exploiting recursive relations and time derivative properties of the Hermite basis functions, an expression of the derivative operator is obtained. The latter is then recursively applied, ensuring that the state-space matrices remain in continuous-time form and making the system suitable for the implementation of steps which are akin to those of the Predictor-Based Subspace IDentification (PBSID) method. This new method, hereby called the Hermite-Domain PBSID (HD-PBSID) method, has the further advantage of avoiding time-shifts by properly scaling the input and output signals. The performance of the proposed approach is illustrated in a simulation study aimed at showing the accuracy of the estimates and at comparing the HD-PBSID method and the Laguerre-projections based Continuous-Time PBSID (CT-PBSID) algorithm.

Jose Antonio Rebollo, Rafael Vazquez, Claudio Bombardelli

Non-Gaussian tails dominate collision probability estimates in conjunction assessment, yet capturing them without Monte Carlo sampling is challenging, especially when process noise is included. We present a closed-form, grid-free solution to the Fokker-Planck equation by proving that an exponential-of-quadratic-form ansatz is structurally preserved under advection and diffusion. The probability density function propagates via a compact ODE system, significantly cheaper than Monte Carlo and without spatial discretization. As an application, the method performs orbit uncertainty propagation under stochastic forcing representative of atmospheric drag. Results demonstrate the method faithfully captures non-Gaussian features, asymmetric tails, and stochastic broadening, matching a Monte Carlo benchmark.