arXiv subjects

Surya Ratna Prakash D

Publications and source records attributed to Surya Ratna Prakash D.

3 recordsLinked to original sources

arXiv2026

Transient Stability of Nonlinear Stochastic Dynamics: A Finite-Amplitude Logarithmic Measure Framework

Spacecraft guidance, navigation, and control systems operating during mission-critical phases such as atmospheric entry, powered descent, and planetary landing require reliable assessment of finite-time transient behavior under stochastic uncertainty. Existing stochastic stability frameworks primarily characterize asymptotic or infinitesimal behavior and therefore provide limited insight into finite-amplitude transient amplification over operationally relevant time horizons. This paper develops a finite-amplitude logarithmic measure for nonlinear Itô stochastic differential equations, extending classical matrix measures from infinitesimal to finite-amplitude perturbation evolution. The proposed framework establishes finite-time mean and variance bounds for logarithmic perturbation growth, derives Chernoff-type probabilistic bounds on transient amplification, and shows that mean transient stability does not necessarily guarantee finite-time pathwise safety. The theory is further extended to projected stochastic dynamics, leading to a transient-risk index that quantitatively balances deterministic contraction and diffusion-induced variability for transient-risk-aware system design. The proposed framework is validated through Monte Carlo simulations and flight-like lunar descent telemetry. The results demonstrate substantially improved prediction of nonlinear transient growth compared with classical Jacobian-based approaches and successfully distinguish trajectories exhibiting similar nominal behavior but significantly different transient-risk characteristics. The proposed framework provides a unified methodology for finite-time transient stability analysis, probabilistic transient-risk assessment, and transient-risk-aware guidance, navigation, and control of nonlinear stochastic aerospace systems.

math.DS↗

arXiv2026

Geometry-Consistent Bayesian Filtering under Structural Model Uncertainty: A Geometric Projection Particle Filter

Nonlinear state estimation under structural model uncertainty remains a fundamental challenge in autonomous Guidance, Navigation, and Control (GNC) systems. Conventional Bayesian filtering separates state propagation from measurement correction, allowing model mismatch to accumulate during propagation, resulting in proposal--likelihood inconsistency, particle degeneracy, and degraded estimation accuracy. Existing approaches primarily improve proposal distributions or weighting strategies without explicitly incorporating measurement geometry into state propagation. This paper introduces a geometry-consistent Bayesian filtering framework that incorporates measurement geometry directly into the propagation process. The nominal drift is projected onto the measurement-consistent subspace, yielding a geometry-consistent proposal while preserving the Bayesian posterior through a rigorous change-of-measure formulation. A Geometric Projection Particle Filter (GPF) is developed together with a geometric co-state that quantifies instantaneous dynamics--measurement inconsistency. Theoretical analysis establishes existence and uniqueness of the projected dynamics, posterior preservation, standard Monte Carlo convergence of the particle approximation, and robustness under structural model uncertainty. The framework is validated using lunar descent navigation under partial observability and persistent model uncertainty. Compared with the bootstrap particle filter and conventional Gaussian filtering methods, GPF consistently achieves higher effective sample size and lower estimation error. These results demonstrate that geometry-consistent propagation provides a principled, computationally efficient, and theoretically grounded framework for robust nonlinear Bayesian filtering.

math.DS↗

arXiv2026

Co-State Based Data Fusion and Risk Aware Filtering for Spacecraft Navigation and Hazard Prediction

This paper develops a co-state based fusion frame work for spacecraft navigation, consistency monitoring, and hazard forecasting. A differential algebraic co-state is introduced as an instantaneous Lagrange multiplier that enforces measurement dynamics compatibility at the differential level and provides a physically interpretable signal of geometric inconsistency. On a longer time scale, co-state and innovation trajectories are used to learn a continuous time Markov generator governing transitions between coarse behavioural regimes, enabling intrinsic probabilistic risk forecasting through mode probabilities and mean first-passage time (MFPT). The resulting architecture unifies geometric projection, stochastic inference, and probabilistic risk assessment in a single online pipeline without requiring predefined fault models, labelled failure data, or heuristic thresholds. The framework is demonstrated on real lunar powered-descent telemetry, where it detects structural internal model inconsistency significantly earlier than physical divergence or statistical inconsistency in an Extended Kalman Filter (EKF). The results show that geometric inconsistency, stochastic drift, and probabilistic risk rise coherently prior to failure, yielding interpretable and operationally meaningful early-warning capability for autonomous landing systems.

math.DS↗