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arxiv: 2512.01182 · v3 · pith:2Z4GVJHNnew · submitted 2025-12-01 · 📡 eess.SY · cs.SY

Autonomous Navigation and Station-Keeping on Near-Rectilinear Halo Orbits

Yuri Shimane , Karl Berntorp , Stefano Di Cairano , Avishai Weiss This is my paper

Pith reviewed 2026-05-25 07:14 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords optical navigationstation-keepingnear-rectilinear halo orbitunscented transformhysteresisDelta-Vephemeris modelMonte Carlo
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The pith

An optical navigation pipeline from synthetic Moon images maintains a spacecraft near a reference near-rectilinear halo orbit while lowering station-keeping Delta-V.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a full pipeline that turns horizon measurements from synthetic lunar images into state estimates inside a navigation filter and then feeds those estimates to a station-keeping controller. It tests two ways of implementing x-axis targeting, adds an unscented-transform prediction step, and inserts a hysteresis rule that further trims propellant use. Monte-Carlo trials show the combined scheme keeps the vehicle near the reference orbit, that total Delta-V drops with the new prediction and hysteresis, and that cumulative cost still varies with maneuver location because the filter's accuracy repeats with the orbit period.

Core claim

The non-iterative horizon-based OPNAV algorithm applied to synthetic Moon images, combined with unscented-transform covariance propagation in the targeting prediction and a hysteresis mechanism in the station-keeping law, produces usable state estimates that allow the spacecraft to track a reference NRHO while reducing cumulative Delta-V, with the remaining cost exhibiting periodic dependence on maneuver location due to the repeating structure of filter accuracy.

What carries the argument

The OPNAV and station-keeping pipeline that converts horizon measurements into filter estimates and applies them to differential-correction or minimization-based x-axis targeting with unscented-transform prediction and hysteresis.

If this is right

  • Station-keeping cost drops when the unscented transform supplies the prediction covariance and when the hysteresis rule is active.
  • Cumulative Delta-V still changes with the orbital location chosen for each maneuver because the filter's estimation accuracy repeats periodically.
  • Both differential-correction and minimization implementations of x-axis targeting keep the spacecraft near the reference orbit, but they respond differently to the added hysteresis.
  • Filter performance varies with sensor field of view and with the true anomaly at which images are taken.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the periodic accuracy pattern holds in flight, mission designers could schedule maneuvers only at orbital phases where the filter is most accurate to minimize propellant.
  • The same image-processing and filtering chain might be tested on other cislunar orbits whose dynamics also produce repeating measurement geometries.
  • Replacing the synthetic images with actual camera data from a spacecraft already in NRHO would directly test whether the reported cost savings survive real lighting and sensor noise.

Load-bearing premise

Synthetic images of the Moon generated in the high-fidelity ephemeris model are representative enough of real sensor data and lighting for the horizon-based algorithm to produce usable measurements.

What would settle it

Flight data from a real NRHO mission showing whether the filter's position and velocity errors and the resulting Delta-V totals match the Monte-Carlo statistics obtained from the synthetic-image pipeline.

Figures

Figures reproduced from arXiv: 2512.01182 by Avishai Weiss, Karl Berntorp, Stefano Di Cairano, Yuri Shimane.

Figure 8
Figure 8. Figure 8: While each run is randomized in terms of initial state error, error on the SRP coefficients of the true dynamics, [PITH_FULL_IMAGE:figures/full_fig_p026_8.png] view at source ↗
read the original abstract

This article develops an optical navigation (OPNAV) and station-keeping pipeline for the near-rectilinear halo orbit (NRHO) in high-fidelity ephemeris model dynamics, using synthetic images of the Moon in a non-iterative horizon-based OPNAV algorithm, applying the result in a navigation filter, and using the obtained estimates in a station-keeping control scheme that keeps the spacecraft in the vicinity of a reference orbit. We study differential correction-based and minimization-based implementations of the so-called x-axis and propose an improved targeting prediction scheme by incorporating the filter's state covariance with an unscented transform. We also introduce a hysteresis mechanism, which improves stationkeeping cost and provides insight into the difference in performance between the differential correction-based and minimization-based approaches. We perform Monte-Carlo experiments to assess the pipeline's tracking and Delta-V performances. We report several key findings, including the variability of the filter performance with the sensor field of view and measurement locations, station-keeping cost reduction achieved by the unscented transform-based prediction and hysteresis, as well as the variability of the cumulative Delta-V as a function of maneuver location due to the periodic structure in the OPNAV-based filter's estimation accuracy.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper develops an optical navigation (OPNAV) and station-keeping pipeline for near-rectilinear halo orbits (NRHO) in high-fidelity ephemeris dynamics. It uses synthetic images of the Moon with a non-iterative horizon-based OPNAV algorithm, integrates results into a navigation filter, and applies estimates to a station-keeping control scheme. The work compares differential correction and minimization-based x-axis targeting methods, introduces an unscented transform-based prediction scheme and a hysteresis mechanism, and evaluates the pipeline through Monte-Carlo experiments, reporting on tracking performance, Delta-V costs, and their variability with sensor field of view, measurement locations, and maneuver locations.

Significance. If the synthetic-image results hold under real conditions, the work provides a practical end-to-end autonomous navigation and station-keeping approach for cislunar missions, with quantitative Monte-Carlo evidence for cost reductions from the unscented-transform predictor and hysteresis. The high-fidelity ephemeris model and explicit treatment of periodic estimation structure are strengths that could inform mission design.

major comments (2)
  1. [Abstract] Abstract (Monte-Carlo experiments paragraph): the reported station-keeping cost reductions and location-dependent cumulative Delta-V variability rest on the non-iterative horizon-based OPNAV producing usable measurements from synthetic Moon images. No sensitivity analysis to unmodeled effects (sensor PSF, albedo variation, stray light, or ephemeris mismatch) is described, which directly affects the credibility of the filter accuracy and Delta-V statistics.
  2. [Abstract] Abstract (station-keeping control scheme description): the claim that the unscented-transform prediction and hysteresis improve performance is load-bearing for the central Delta-V reduction result, yet the manuscript provides no explicit before/after quantification (e.g., mean and variance of total Delta-V with and without each feature across the Monte-Carlo ensemble).
minor comments (1)
  1. [Abstract] The abstract is a single dense paragraph; splitting the description of methods, improvements, and findings would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract and the need for explicit quantification and clearer acknowledgment of modeling assumptions. We address each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract (Monte-Carlo experiments paragraph): the reported station-keeping cost reductions and location-dependent cumulative Delta-V variability rest on the non-iterative horizon-based OPNAV producing usable measurements from synthetic Moon images. No sensitivity analysis to unmodeled effects (sensor PSF, albedo variation, stray light, or ephemeris mismatch) is described, which directly affects the credibility of the filter accuracy and Delta-V statistics.

    Authors: We agree that the Monte-Carlo results rely on idealized synthetic images and contain no sensitivity analysis to the listed unmodeled effects. This is a real limitation of the present study, which demonstrates the pipeline under controlled conditions rather than claiming robustness to all sensor and environmental perturbations. In revision we will qualify the abstract accordingly and add an explicit limitations paragraph in the conclusions that discusses the potential impact of PSF, albedo, stray light, and ephemeris mismatch on the reported statistics, while identifying these analyses as future work. revision: partial

  2. Referee: [Abstract] Abstract (station-keeping control scheme description): the claim that the unscented-transform prediction and hysteresis improve performance is load-bearing for the central Delta-V reduction result, yet the manuscript provides no explicit before/after quantification (e.g., mean and variance of total Delta-V with and without each feature across the Monte-Carlo ensemble).

    Authors: The results section already contains Monte-Carlo ensemble statistics that compare station-keeping costs across configurations with and without the unscented-transform predictor and hysteresis. To make the abstract self-contained and to directly support the central claim, we will revise the abstract to include the explicit numerical reductions (mean and variance of total Delta-V) obtained from those ensembles. revision: yes

Circularity Check

0 steps flagged

No significant circularity; results from forward Monte-Carlo simulation of synthetic images and filter interactions

full rationale

The paper's central results (NRHO tracking performance, Delta-V reductions via unscented transform and hysteresis, location-dependent costs) are obtained by running a closed-loop simulation pipeline: synthetic Moon images generated in high-fidelity ephemeris dynamics are processed by a non-iterative horizon OPNAV algorithm, fed into a navigation filter, and used by station-keeping controllers. No equations or reported metrics are obtained by fitting parameters to the target Delta-V or tracking statistics themselves; the Monte-Carlo outcomes are independent forward predictions under the stated modeling assumptions. No self-citations are invoked as load-bearing uniqueness theorems, no ansatzes are smuggled, and no predictions reduce by construction to quantities defined from the same data. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit list of fitted parameters or new axioms; the pipeline relies on standard orbital dynamics, image processing assumptions, and filter models whose details are not supplied.

pith-pipeline@v0.9.0 · 5751 in / 1249 out tokens · 40737 ms · 2026-05-25T07:14:08.588346+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    We develop an end-to-end pipeline that consists of a navigation filter relying solely on measurements from processing realistic synthetic images of the Moon, and extensions to the x-axis crossing control algorithm that uses the recursively filtered state estimate to compute control actions.

  • IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    The unscented transform-based targeting scheme... hysteresis mechanism... variability of the cumulative ΔV as a function of maneuver location due to the periodic structure in the OPNAV-based filter's estimation accuracy.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

44 extracted references · 44 canonical work pages

  1. [1]

    Gateway Destination Orbit Model: A Continuous 15 Year NRHO Reference Trajectory,

    Lee, D. E., “Gateway Destination Orbit Model: A Continuous 15 Year NRHO Reference Trajectory,” Tech. rep., NASA, 2019

    work page 2019

  2. [2]

    Near rectilinear halo orbits and nearby higher-period dynamical structures: orbital stability and resonance properties,

    Zimovan-Spreen, E. M., Howell, K. C., and Davis, D. C., “Near rectilinear halo orbits and nearby higher-period dynamical structures: orbital stability and resonance properties,”Celestial Mechanics and Dynamical Astronomy, Vol. 132, No. 5, 2020. https://doi.org/10.1007/s10569-020-09968-2

    work page doi:10.1007/s10569-020-09968-2 2020

  3. [3]

    Dynamical Structures Nearby NRHOs with Applications to Transfer Design in Cislunar Space,

    Zimovan-Spreen, E. M., Howell, K. C., and Davis, D. C., “Dynamical Structures Nearby NRHOs with Applications to Transfer Design in Cislunar Space,”Journal of the Astronautical Sciences, Vol. 69, No. 3, 2022, pp. 718–744. https://doi.org/10.1007/s40295-022-00320-4

    work page doi:10.1007/s40295-022-00320-4 2022

  4. [4]

    Noniterative Horizon-Based Optical Navigation by Cholesky Factorization,

    Christian, J. A., and Robinson, S. B., “Noniterative Horizon-Based Optical Navigation by Cholesky Factorization,”Journal of Guidance, Control, and Dynamics, Vol. 39, 2016, pp. 2757–2765. https://doi.org/10.2514/1.G000539, URL https: //arc.aiaa.org/doi/10.2514/1.G000539

    work page doi:10.2514/1.g000539 2016

  5. [5]

    Accurate Planetary Limb Localization for Image-Based Spacecraft Navigation,

    Christian, J. A., “Accurate Planetary Limb Localization for Image-Based Spacecraft Navigation,”Journal of Spacecraft and Rockets, Vol. 54, 2017, pp. 708–730. https://doi.org/10.2514/1.A33692, URL https://arc.aiaa.org/doi/10.2514/1.A33692

    work page doi:10.2514/1.a33692 2017

  6. [6]

    A Tutorial on Horizon-Based Optical Navigation and Attitude Determination With Space Imaging Systems,

    Christian, J. A., “A Tutorial on Horizon-Based Optical Navigation and Attitude Determination With Space Imaging Systems,” IEEE Access, Vol. 9, 2021, pp. 19819–19853. https://doi.org/10.1109/ACCESS.2021.3051914

    work page doi:10.1109/access.2021.3051914 2021

  7. [7]

    Autonomous optical navigation for the lunar meteoroid impacts observer,

    Franzese, V., Di Lizia, P., and Topputo, F., “Autonomous optical navigation for the lunar meteoroid impacts observer,”Journal of Guidance, Control, and Dynamics, Vol. 42, No. 7, 2019, pp. 1579–1586. https://doi.org/10.2514/1.G003999

    work page doi:10.2514/1.g003999 2019

  8. [8]

    Moon Limb-Based Autonomous Optical Navigation Using Star Trackers,

    Balossi, C., Piccolo, F., Panicucci, P., Pugliatti, M., Topputo, F., and Capolupo, F., “Moon Limb-Based Autonomous Optical Navigation Using Star Trackers,”46th Rocky Mountain AAS GN&C Conference, 2024, pp. 1–19. 36

    work page 2024

  9. [9]

    Sensorconfigurationtradestudyfornavigationinnearrectilinearhaloorbits,

    Yun, S., Tuggle, K., Zanetti, R., andD’souza, C., “Sensorconfigurationtradestudyfornavigationinnearrectilinearhaloorbits,” Advances in the Astronautical Sciences, Vol. 171, Univelt Inc., 2020, pp. 2799–2812. https://doi.org/10.1007/s40295-020- 00224-1

    work page doi:10.1007/s40295-020- 2020

  10. [10]

    Investigation on Autonomous Orbit Determination in Cislunar Space via GNSS and Horizon-based Measurements,

    Qi, D. C., and Oguri, K., “Investigation on Autonomous Orbit Determination in Cislunar Space via GNSS and Horizon-based Measurements,”AAS/AIAA Space Flight Mechanics Meeting, 2023. URL https://www.researchgate.net/publication/368663934

    work page arXiv 2023

  11. [11]

    Characterization of horizon-based opticalnavigationmeasurementerrorsaroundtheMoon: Patterns,theirevolution,andamodelingapproach,

    Machuca, P., Wu, C. X., Gleason, D., Topolcsik, E., Lozano Ortega, R., and Linares, R., “Characterization of horizon-based opticalnavigationmeasurementerrorsaroundtheMoon: Patterns,theirevolution,andamodelingapproach,”Actaastronautica, Vol. 239, 2026, pp. 912–931. https://doi.org/10.1016/j.actaastro.2025.11.058

    work page doi:10.1016/j.actaastro.2025.11.058 2026

  12. [12]

    EQUULEUS Trajectory Design,

    Oguri, K., Oshima, K., Campagnola, S., Kakihara, K., Ozaki, N., Baresi, N., Kawakatsu, Y., and Funase, R., “EQUULEUS Trajectory Design,”Journal of Astronautical Sciences, 2020. https://doi.org/10.1007/s40295-019-00206-y

    work page doi:10.1007/s40295-019-00206-y 2020

  13. [13]

    Artemis I Optical Navigation System Performance,

    Inman, R., Holt, G., Christian, J., Smith, K., and D’Souza, C., “Artemis I Optical Navigation System Performance,”AIAA SciTech Forum and Exposition, 2024. https://doi.org/10.2514/6.2024-0514

    work page doi:10.2514/6.2024-0514 2024

  14. [14]

    G., Ready, W

    Krause, M., Thrasher, A., Soni, P., Smego, L., Isaac, R., Nolan, J., Pledger, M., Lightsey, E. G., Ready, W. J., and Christian, J., LONEStar: The Lunar Flashlight Optical Navigation Experiment, Springer US, 2024. https://doi.org/10.1007/s40295-024- 00452-9, URL http://arxiv.org/abs/2401.12198

    work page doi:10.1007/s40295-024- 2024

  15. [15]

    Assessing Horizon-based Optical Navigation in a Near Rectilinear Halo Orbit,

    Givens, M., Caudill, M., Bolliger, M., Qi, D., and Parker, J., “Assessing Horizon-based Optical Navigation in a Near Rectilinear Halo Orbit,”47th Rocky Mountain AAS GN&C Conference, 2025

    work page 2025

  16. [16]

    LUMIO: A CubeSat for observing and characterizing micro-meteoroid impacts on the Lunar far side,

    Cervone, A., Topputo, F., Speretta, S., Menicucci, A., Turan, E., Di Lizia, P., Massari, M., Franzese, V., Giordano, C., Merisio, G., Labate, D., Pilato, G., Costa, E., Bertels, E., Thorvaldsen, A., Kukharenka, A., Vennekens, J., and Walker, R., “LUMIO: A CubeSat for observing and characterizing micro-meteoroid impacts on the Lunar far side,”Acta astronau...

    work page doi:10.1016/j.actaastro.2022.03.032 2022

  17. [17]

    Survey of station-keeping techniques for libration point orbits,

    Shirobokov, M., Trofimov, S., and Ovchinnikov, M., “Survey of station-keeping techniques for libration point orbits,”Journal of Guidance, Control, and Dynamics, Vol. 40, No. 5, 2017, pp. 1085–1105. https://doi.org/10.2514/1.G001850

    work page doi:10.2514/1.g001850 2017

  18. [18]

    Chance-Constrained Control for Safe Spacecraft Autonomy: Convex Programming Approach,

    Oguri, K., “Chance-Constrained Control for Safe Spacecraft Autonomy: Convex Programming Approach,”2024 American Control Conference (ACC), 2024, pp. 2318–2324. https://doi.org/10.23919/ACC60939.2024.10645008

    work page doi:10.23919/acc60939.2024.10645008 2024

  19. [19]

    Orbit MaintenanceandNavigationofHumanSpacecraftatCislunarNearRectilinearHaloOrbits,

    Davis, D., Bhatt, S., Howell, K., Jang, J.-W., Whitley, R., Clark, F., Guzzetti, D., Zimovan, E., and Barton, G., “Orbit MaintenanceandNavigationofHumanSpacecraftatCislunarNearRectilinearHaloOrbits,”AAS/AIAASpaceFlightMechanics Meeting, 2017

    work page 2017

  20. [20]

    StationkeepingAnalysisforSpacecraftinLunarNearRectilinear Halo Orbits,

    Guzzetti,D.,Zimovan,E.M.,Howell,K.C.,andDavis,D.C.,“StationkeepingAnalysisforSpacecraftinLunarNearRectilinear Halo Orbits,” 2017. 37

    work page 2017

  21. [21]

    Stationkeeping, Orbit Determination, and Attitude Control for Spacecraft in Near Rectilinear Halo Orbits,

    Newman, C. P., Davis, D. C., Whitley, R. J., Guinn, J. R., and Ryne, M. S., “Stationkeeping, Orbit Determination, and Attitude Control for Spacecraft in Near Rectilinear Halo Orbits,”AAS Astrodynamics Specialists Conference, 2018. URL https://ntrs.nasa.gov/search.jsp?R=20180006800

    work page 2018

  22. [22]

    Orbit Maintenance Burn Details for Spacecraft in a Near Rectilinear Halo Orbit,

    Davis, D. C., Scheuerle, S. T., Williams, D. A., Miguel, F. S., Zimovan-Spreen, E. M., and Howell, K. C., “Orbit Maintenance Burn Details for Spacecraft in a Near Rectilinear Halo Orbit,”AAS/AIAA Astrodynamics Specialists Conference, 2022

    work page 2022

  23. [23]

    Cislunar autonomous positioning system technology operations and navigation experiment(Capstone),

    Cheetham, B., Gardner, T., and Forsman, A., “Cislunar autonomous positioning system technology operations and navigation experiment(Capstone),”AcceleratingSpaceCommerce,Exploration,andNewDiscoveryconference,ASCEND2021,American Institute of Aeronautics and Astronautics Inc, AIAA, 2021. https://doi.org/10.2514/6.2021-4128

    work page doi:10.2514/6.2021-4128 2021

  24. [24]

    Sequential linearization-based station keeping with optical navigation for NRHO,

    Elango, P., Di Cairano, S., Berntorp, K., and Weiss, A., “Sequential linearization-based station keeping with optical navigation for NRHO,”AAS/AIAA Astrodynamics Specialist Conference, 2022

    work page 2022

  25. [25]

    Numerically optimal Runge-Kutta pairs with interpolants,

    Verner, J. H., “Numerically optimal Runge-Kutta pairs with interpolants,”Numerical Algorithms, Vol. 53, 2010, pp. 383–396. https://doi.org/10.1007/s11075-009-9290-3

    work page doi:10.1007/s11075-009-9290-3 2010

  26. [26]

    Gough, B.,GNU scientific library reference manual, Network Theory Ltd., 2009

    work page 2009

  27. [27]

    High-Fidelity Simulation of Horizon- Based Optical Navigation with Open-Source Software,

    Shimane, Y., Miraldo, P., Berntorp, K., Greiff, M., Elango, P., and Weiss, A., “High-Fidelity Simulation of Horizon- Based Optical Navigation with Open-Source Software,”International Astronautical Congress (IAC), 2023. URL https: //www.merl.com/publications/TR2023-128

    work page 2023

  28. [29]

    Hartley, R., and Zisserman, A.,Multiple View Geometry in Computer Vision, 2nd ed., Cambridge University Press, New York, NY, USA, 2003

    work page 2003

  29. [30]

    Geometric Calibration of the Orion Optical Navigation Camera using Star Field Images,

    Christian, J. A., Benhacine, L., Hikes, J., and D’Souza, C., “Geometric Calibration of the Orion Optical Navigation Camera using Star Field Images,”Journal of Astronautical Sciences, Vol. 63, No. 4, 2016, pp. 335–353. https://doi.org/10.1007/s40295- 016-0091-3

    work page doi:10.1007/s40295- 2016

  30. [32]

    Optical Navigation Using Iterative Horizon Reprojection,

    Christian, J. A., “Optical Navigation Using Iterative Horizon Reprojection,”Journal of Guidance, Control, and Dynamics, Vol. 39, 2016, pp. 1092–1103. https://doi.org/10.2514/1.G001569, URL https://arc.aiaa.org/doi/10.2514/1.G001569. [33]Blender - a 3D modelling and rendering package, Blender Foundation, 2018. URL http://www.blender.org

    work page doi:10.2514/1.g001569 2016

  31. [33]

    Navigation Filter Best Practices,

    Carpenter, J. R., and D’souza, C. N., “Navigation Filter Best Practices,” Tech. rep., 2018. URL http://www.sti.nasa.gov. 38

    work page 2018

  32. [34]

    17, Cambridge university press, 2023

    Särkkä, S., and Svensson, L.,Bayesian filtering and smoothing, Vol. 17, Cambridge university press, 2023

    work page 2023

  33. [35]

    D., Schutz, B

    Tapley, B. D., Schutz, B. E., and Born, G. H.,Statistical Orbit Determination, Elsevier Academic Press, 2004

    work page 2004

  34. [36]

    On unscented Kalman filtering for state estimation of continuous-time nonlinear systems,

    Särkkä, S., “On unscented Kalman filtering for state estimation of continuous-time nonlinear systems,”IEEE Transactions on Automatic Control, Vol. 52, No. 9, 2007, pp. 1631–1641. https://doi.org/10.1109/TAC.2007.904453

    work page doi:10.1109/tac.2007.904453 2007

  35. [37]

    Anonymous feature-based terrain relative navigation,

    McCabe, J. S., and DeMars, K. J., “Anonymous feature-based terrain relative navigation,”Journal of guidance, control, and dynamics: a publication of the American Institute of Aeronautics and Astronautics devoted to the technology of dynamics and control, Vol. 43, No. 3, 2020, pp. 410–421. https://doi.org/10.2514/1.G004423

    work page doi:10.2514/1.g004423 2020

  36. [38]

    Lunar Crater Identification in Digital Images,

    Christian, J. A., Derksen, H., and Watkins, R., “Lunar Crater Identification in Digital Images,”Journal of Astronautical Sciences, Vol. 68, No. 4, 2021, pp. 1056–1144. https://doi.org/10.1007/s40295-021-00287-8

    work page doi:10.1007/s40295-021-00287-8 2021

  37. [39]

    Image-BasedLunarTerrainRelativeNavigationWithout aMap: Measurements,

    Christian,J.A.,Hong,L.,McKee,P.,Christensen,R.,andCrain,T.P.,“Image-BasedLunarTerrainRelativeNavigationWithout aMap: Measurements,”Journalofspacecraftandrockets,Vol.58,No.1,2021,pp.164–181. https://doi.org/10.2514/1.A34875

    work page doi:10.2514/1.a34875 2021

  38. [40]

    Lidar Odometry for Lunar Terrain Relative Navigation,

    De Vries, C., Christian, J., Hansen, M., and Crain, T., “Lidar Odometry for Lunar Terrain Relative Navigation,”AAS/AIAA Astrodynamics Specialist Conference, 2022

    work page 2022

  39. [41]

    Sensor fusion for autonomous orbit determination and time synchronization in lunar orbit,

    Vila, G. C., and Gao, G., “Sensor fusion for autonomous orbit determination and time synchronization in lunar orbit,”2025 IEEE Aerospace Conference, IEEE, 2025, pp. 1–12. https://doi.org/10.1109/aero63441.2025.11068682

    work page doi:10.1109/aero63441.2025.11068682 2025

  40. [42]

    LocalEigenmotionControlforNearRectilinearHaloOrbits,

    Elango,P.,DiCairano,S.,Kalabic,U.,andWeiss,A.,“LocalEigenmotionControlforNearRectilinearHaloOrbits,”Proceedings oftheAmericanControlConference,Vol.2022-June,2022,pp.1822–1827. https://doi.org/10.23919/ACC53348.2022.9867672

    work page doi:10.23919/acc53348.2022.9867672 2022

  41. [43]

    Optimization-BasedPhase-ConstrainedStation-KeepingControlonLibrationPointOrbit,

    Shimane,Y.,Ho,K.,andWeiss,A.,“Optimization-BasedPhase-ConstrainedStation-KeepingControlonLibrationPointOrbit,” AAS/AIAA Astrodynamics Specialist Conference, 2024, pp. 1–19

    work page 2024

  42. [44]

    Revolution-Spaced Output-Feedback Model Predictive Control for Station Keeping on Near-Rectilinear Halo Orbits,

    Shimane, Y., Di Cairano, S., Ho, K., and Weiss, A., “Revolution-Spaced Output-Feedback Model Predictive Control for Station Keeping on Near-Rectilinear Halo Orbits,”IEEE Transactions on Control Systems Technology, 2025. https: //doi.org/10.1109/TCST.2025.3614324

    work page doi:10.1109/tcst.2025.3614324 2025

  43. [45]

    Leveraging stretching directions for stationkeeping in Earth-Moon halo orbits,

    Muralidharan, V., and Howell, K. C., “Leveraging stretching directions for stationkeeping in Earth-Moon halo orbits,” Advances in Space Research, Vol. 69, No. 1, 2022, pp. 620–646. https://doi.org/10.1016/j.asr.2021.10.028, URL https: //doi.org/10.1016/j.asr.2021.10.028

    work page doi:10.1016/j.asr.2021.10.028 2022

  44. [46]

    H., and Combettes, P

    Bauschke, H. H., and Combettes, P. L.,Convex Analysis and Monotone Operator Theory in Hilbert Spaces, 1st ed., Springer Publishing Company, Incorporated, 2011. 39

    work page 2011