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UID:198@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20260527T150000
DTEND;TZID=Asia/Kolkata:20260527T160000
DTSTAMP:20260523T144457Z
URL:https://cds.iisc.ac.in/events/ph-d-thesis-colloquium-102-cds-28-may-20
 26-geometry-consistent-and-integrity-aware-navigation-for-autonomous-space
 craft-under-partial-observability-and-model-uncertainty/
SUMMARY:Change in Date : Ph.D: Thesis Colloquium: 102 : CDS: 27\, May 2026 
 “Geometry-Consistent and Integrity-Aware Navigation for Autonomous Space
 craft under Partial Observability and Model Uncertainty”
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nPh.D. Thesis Col
 loquium\n\n\n\nSpeaker: Mr. Surya Ratna Prakash Dumpa\nS.R. Number: 06-18-
 00-11-12-20-1-18701\nTitle: “Geometry-Consistent and Integrity-Aware Nav
 igation for Autonomous Spacecraft under Partial Observability and Model Un
 certainty”\nResearch Supervisor: Prof. Soumyendu Raha\nDate &amp\; Time 
 : May 27\, 2026 (Wednesday)\, 03:00 PM\nVenue : #102\, CDS Seminar Hall\n\
 n\n\nABSTRACT\nAutonomous spacecraft navigation during powered descent and
  planetary landing operates under severe uncertainty\, partial observabili
 ty\, and strict finite-time safety requirements. During such mission phase
 s\, navigation systems must reconcile nonlinear dynamics with noisy and in
 complete measurements while remaining robust to structural model mismatch\
 , estimator degradation\, and transient instability. Classical filtering a
 rchitectures\, including Kalman filtering and standard particle filtering\
 , primarily rely on posterior correction after state propagation and are t
 ypically analyzed using asymptotic stability notions. These approaches may
  become inadequate in autonomy-critical regimes where local geometric inco
 nsistency\, particle degeneracy\, and short-horizon perturbation growth de
 termine mission success before asymptotic behaviour becomes relevant.\n\nT
 his thesis develops a unified geometric and stochastic framework for integ
 rity-aware autonomous spacecraft navigation in which geometric consistency
 \, estimator integrity\, probabilistic hazard forecasting\, and finite-tim
 e transient stability are treated as interconnected components of the same
  problem.\n\nFirst\, a co-state-based differential--algebraic framework is
  developed to enforce local compatibility between system dynamics and meas
 urement geometry under partial observability. The resulting geometric co-s
 tate variable acts as an intrinsic consistency signal linking nominal prop
 agation with the measurement-consistent manifold. This signal is further u
 sed to construct stochastic regime abstractions and continuous-time hazard
  forecasting models\, enabling probabilistic prediction of degraded operat
 ing modes through regime occupancy probabilities and mean first-passage in
 dicators.\n\nSecond\, an integrity-aware nonlinear filtering architecture\
 , termed the Geometric Projection Particle Filter (GPF)\, is introduced fo
 r nonlinear and non-Gaussian state estimation under persistent model uncer
 tainty. Unlike conventional bootstrap particle filters\, the proposed meth
 od projects nominal drift dynamics directly onto the measurement-consisten
 t subspace during propagation\, creating a geometry-aware proposal process
  that reduces proposal--likelihood mismatch prior to weighting. This suppr
 esses particle degeneracy\, improves effective sample size retention\, and
  maintains bounded estimation error in regimes where standard particle fil
 ters diverge.\n\nThird\, a logarithmic-norm-based framework is developed f
 or analyzing finite-time transient stability of nonlinear It^o stochastic 
 systems and projected data-constrained dynamics. By extending matrix measu
 res in a Lipschitz sense\, the approach provides tractable characterizatio
 n of perturbation growth without requiring explicit Lyapunov function cons
 truction. The analysis clarifies the distinction between mean stability an
 d pathwise finite-time safety\, showing that stochastic diffusion induces 
 nonzero probability of critical transient excursions even when average beh
 aviour remains stable.\n\nThe proposed methods are validated using lunar l
 ander descent navigation scenarios with partial observability and persiste
 nt model mismatch. Results demonstrate early hazard detection\, improved e
 stimator robustness\, significant reduction in particle degeneracy\, inter
 pretable probabilistic hazard forecasting\, and stronger finite-time safet
 y characterization compared with classical filtering and asymptotic stabil
 ity approaches.\n\nThese results establish that autonomous navigation shou
 ld be treated not only as a state estimation problem\, but as a coupled pr
 oblem of geometric consistency\, estimator integrity\, stochastic hazard e
 volution\, and finite-time operational safety. The thesis provides theoret
 ical foundations and practical algorithmic realizations for integrity-awar
 e autonomous spacecraft navigation.\n\n\n\nALL ARE WELCOME
CATEGORIES:Events,Ph.D. Thesis Colloquium
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