Pith. sign in

REVIEW 3 major objections 6 minor 32 references

Cooperative Multi-spacecraft Observation of Incoming Space Threats

T0 review · 3 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A constellation of small calibrated cameras can localize an incoming meteor by fitting best-fit planes to pixel directions and solving a linear system, with simulation putting the maximum error near 300 km for three spacecraft with…

desk verdict Plausible algorithm, but the headline 300 km localization number is an artifact of an underspecified and overly favorable simulation geometry; the design study deserves revision, not a desk reject. read the letter →

arxiv 1909.01814 v1 pith:FQVJRSBE submitted 2019-09-02 astro-ph.IM astro-ph.EPphysics.space-ph

Ravi teja Nallapu , Jekan Thangavelautham This is my paper · ORCID
classification astro-ph.IMastro-ph.EPphysics.space-ph
keywords meteorlocalizationspacecraftconstellationtriangulationspacesituationalawarenesssmallsatellitesMonteCarlosimulationWalker-Deltageneticalgorithmoptimization
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper attempts to show that a space-based network of small imaging spacecraft can autonomously locate incoming meteors and reentering debris, a job now done from ground observatories that are limited by field of view and atmospheric interference. The localization method treats each camera pixel as a direction measurement, fits a best-fit plane to each spacecraft's observations, intersects two planes to get the meteor's radiant line, and then solves an overdetermined linear system to estimate each point on the trail. Monte Carlo simulation across pointing errors and spacecraft counts finds that three spacecraft with pointing accuracy better than 2 degrees localize an event with a maximum error of about 300 km, and the error falls as more spacecraft join. The paper also solves the constellation design problem by evolutionary optimization, producing a 44-spacecraft design that meets an 85% detection requirement even when 10% of the spacecraft are out. A sympathetic reading is that the paper supplies the full chain from camera pixels to localized event to robust constellation, with simulation evidence at each step.

What carries the argument

The carrying object is the plane-fit-radiant-and-linear-system localization algorithm. For each spacecraft, closed-form least-squares expressions fit the plane normal that best matches the measured pixel directions; the meteor radiant is the normalized cross product of the two best-fit plane normals; then each trail point is recovered as the least-squares solution of the stacked plane equations. This reduces a nonlinear triangulation problem to a linear solve that runs onboard and requires no common fixation point, only calibrated cameras and knowledge of spacecraft positions.

What would settle it

Run the same Monte Carlo simulation with meteor entry points drawn uniformly over the full 70-140 km shell instead of starting at the observer centroid, and with spacecraft separated according to the actual optimized Walker-$\Delta$ ephemerides; if the 90th percentile of maximum error stays near 300 km at 2-degree pointing in all geometries the claim holds, and if it does not, the bound is an artifact of the favorable geometry.

Watch

Extended reading notes

Core claim

On its own terms, the paper claims that multipoint meteor localization reduces to a linear algebra problem. Calibrated cameras yield unit vectors for each pixel; a closed-form plane fit per spacecraft gives plane normals; the radiant is the normalized cross product of two plane normals; and the physical location of any trail point is the least-squares solution of the stacked plane equations. The simulation shows a sharp error cliff: two spacecraft leave mean maximum errors in the 1,000 km regime, while three spacecraft with less than 2 degrees of pointing error bring the maximum error to about 300 km, with the 1-$\sigma$ spread dropping from about 500 km to about 100 km. The paper further claims that the constellation design problem, with requirements on detection probability and robustness to spacecraft outages, can be solved as a mixed-integer genetic algorithm optimization, and that the resulting 44-spacecraft Walker-$\Delta$ pattern meets an 85% effectiveness target in both static Monte Carlo checks and dynamic orbit simulations.

Load-bearing premise

The quoted 300 km error assumes every simulated meteor starts at the centroid of the observing spacecraft, with the spacecraft clustered in latitude and longitude; if a real meteor appears outside that favorable geometry, the triangulation error can be larger.

Editorial extensions

If this is right

  • At least three observing spacecraft are needed to make localization meaningful; two spacecraft leave mean maximum errors around 1,000 km.
  • With pointing accuracy better than 2 degrees, a three-spacecraft network achieves maximum localization error around 300 km, and the error decreases as more spacecraft observe the event.
  • A 44-spacecraft Walker-Delta pattern with a 14:1 repeat ground track, 66.8-degree inclination, and 0.035 eccentricity meets an 85% detection-effectiveness requirement even with 10% of satellites defunct.
  • Because the method does not require spacecraft to stare at the same point, wide-field nadir-pointing cameras on small satellites can supply the needed multipoint observations.
  • Localizing every point on a meteor trail yields trajectory orientation that can be propagated backward to infer the meteor's source and forward to predict dark flight and possible meteorite fall locations.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: the same plane-fit least-squares structure transfers directly to tracking accelerating objects such as reentering debris, because the per-pixel linear solve makes no constant-velocity assumption; appending a dynamics model per frame should recover velocity and acceleration as well as position.
  • Beyond the paper: the quoted 300 km bound is tied to the simulated geometry in which each meteor starts at the centroid of the observing spacecraft; a testable extension is to repeat the Monte Carlo with entry points spread across the full Earth disk and with spacecraft separated according to the actual optimized constellation ephemerides, which should expose how error grows with off-boresight ang
  • Beyond the paper: 2-degree pointing is looser than typical star-tracker performance, so in an operational system the dominant error source may shift from pointing to camera calibration and inter-spacecraft timing synchronization; a hardware-in-the-loop testbed would show which term actually sets the error floor.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper proposes a cooperative space-based meteor monitoring architecture. It derives a localization algorithm in which each spacecraft fits a plane to the observed pixel directions of a meteor trail, the radiant is obtained from the intersection of two such planes, and each trail point is recovered by solving the overdetermined linear system in Eq. (9). The algorithm is tested in a MATLAB Monte Carlo simulation that varies the number of observing spacecraft and pointing accuracy, yielding error maps in Fig. 7. The authors then pose the constellation design as a mixed-integer optimization that minimizes the number of spacecraft subject to a detection-effectiveness requirement and an aperture constraint, and validate the resulting 44-spacecraft Walker-Delta design in STK by checking field-of-view access to random meteor events. The headline quantitative claim is that three spacecraft with pointing accuracies below 2 deg can localize a meteor event with a maximum error of about 300 km.

Significance. If the quantitative claims are supported, the paper is a useful contribution to space-based space situational awareness: it provides a closed-form, computationally lightweight localization algorithm suitable for onboard implementation, and it couples that algorithm to a constellation design framework that explicitly accounts for spacecraft outages and camera aperture limits. The paper is self-contained and the derivation of Eqs. (3)-(9) is a genuine strength, as is the reproducibility of the Monte Carlo and STK workflows. However, the central error numbers are currently established only under a favorable and incompletely specified simulation geometry, and the constellation validation never exercises localization error in the actual designed geometry. The significance is therefore conditional on additional work that makes the error analysis representative of realistic and designed observing geometries.

major comments (3)
  1. [Section 3, Accuracy of localization; Fig. 7; Table 1] The Monte Carlo geometry used to produce the error maps is underspecified and favorable. The text states that the spacecraft are 'located relatively closely with respect to each other such that their latitudes and longitudes have bounded random differences,' but it never gives the distribution or numerical bounds of those differences, nor the resulting spacecraft separations. It also states that 'the head of the meteor is generated from the centroid of the imaging spacecraft,' which places the target inside the convex hull of the observers and is the geometry that minimizes triangulation dilution of precision. Because Fig. 7 and the Section 5 claim of roughly 300 km error for N_ss = 3 and P_as < 2 deg are generated under this geometry, the reported error numbers do not support localization accuracy for arbitrary events or for the subsequently designed Walker constellation. Please quantify the spacecraft baseline distribution, sample events across the full field of view and outside the observer cluster, and report localization error as a function of baseline and target position.
  2. [Section 4, Design validation; Eq. (17)] The optimal constellation design is scored only by detection effectiveness P_eff, defined in Eq. (15) as the fraction of Monte Carlo events for which at least N_ss operating spacecraft have the meteor in their field of view, and by the aperture constraint. Localization error does not appear in the optimization objective or constraints. The STK 'Design validation' subsection verifies chain access, i.e., that meteors are visible to at least three spacecraft, but it does not compute the localization error that would result from the actual 44:11/4 Walker geometry, including the random outages modeled in Section 3. The conclusion that the designed constellation supports the 300 km localization accuracy is therefore not established. Please evaluate localization error on events observed by the designed constellation, with realistic spacecraft positions and outage patterns.
  3. [Section 3, Eqs. (3)-(10)] The error study is internally circular and lacks external validation. The measurement model in Eq. (10) adds independent uniform errors to right ascension and declination, and the localization error is then computed by feeding these synthetic measurements into the same algorithm under test. This is a necessary self-consistency check, but it does not validate the algorithm against real meteor observations, independent ground-based networks, or a more realistic sensor model that includes centroiding error, correlated attitude error, timing error, and photon noise. The paper's claim that the system meets 'realistic detection and accuracy requirements' would be substantially strengthened by a comparison with known meteor events or by a sensor model calibrated to the low-to-mid-tier cameras assumed in the abstract.
minor comments (6)
  1. [Section 3, Accuracy of localization] There is a typo: 'How accurate can is the estimated position?' should read 'How accurate can the estimated position be?'
  2. [Table 1 and Section 3] The notation for the number of meteor observation points is inconsistent: the text refers to N_pts while Table 1 uses N_prs; please unify the symbol.
  3. [Eqs. (5)-(6)] The typesetting of primes and subscripts in the plane-coefficient equations is garbled (e.g., c' and d' appear without clear subscripts, and the normal vector is written with an i-hat that should be n-hat). Please carefully reset these equations.
  4. [Fig. 7] The axes and color scale of the error contour plots are not labeled, and the text should state explicitly whether the plotted quantity is the mean of the per-event maximum error, a percentile, or a different statistic of Eq. (11).
  5. [Section 5, Conclusion] The headline 'maximum error of about 300 km' should be qualified with the statistic it represents; Section 4 reports the 'mean maximum error,' so the conclusion should say whether 300 km is the mean, median, or a high quantile of the max-error distribution.
  6. [References] The reference list contains duplicates and numbering inconsistencies; for example, the Murad and Williams edited volume appears as both [4] and [22], and several earlier papers by the authors are cited multiple times. Please reconcile the citation numbering.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the localization error is a forward Monte Carlo statistic, not a fitted or self-referential input.

full rationale

The derivation chain is self-contained and non-circular. The localization algorithm (Eqs. 3-9) is a geometric least-squares triangulation: Eq. 9 is solved from plane normals computed from simulated pixel directions, and the Monte Carlo then injects independent pointing noise (Eq. 10) into this forward map and reads off the resulting errors (Eq. 11). No parameter is fitted to the error output, and the headline 300 km figure is a simulation statistic, not a fitted or renamed input. The constellation coverage and optimization are checked by a separate FoV-access Monte Carlo and independently re-run in STK, so that validation does not reduce to the localization algorithm's own output. The self-citations (SWIMSat, prior detectors, attitude control) provide context and prior components; the load-bearing derivation explicitly cites Ceplecha [6] for the plane-fitting step and is not justified by self-citation. The unspecified 'bounded random differences' and the centroid-generated meteor head are validation-geometry limitations, not circularity: they may make the reported error optimistic, but they do not make the prediction equivalent to its inputs by construction. Thus no circular step is present.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The central error estimates are produced entirely by the paper's own simulation, which assumes calibrated cameras, a straight-line meteor, known spacecraft positions, and a favorable observing geometry. The constellation size and design are outputs of a genetic algorithm evaluated on the same type of simulated events, so the validation is self-referential. The only external anchor is Ceplecha's plane-fitting geometry from Reference [6].

free parameters (6)
  • Spacecraft altitude h_ss = 450 km
    Fixed altitude for all simulated observing spacecraft (Table 1); the FoV geometry and localization error depend on it.
  • Meteor trail length l_M = 1 km
    Simulated meteors are drawn as 1 km straight lines (Table 1); short trails make the radiant fit more sensitive to pointing noise.
  • Number of trail sample points N_pts = 6
    Each simulated meteor trail is sampled into 6 points that are 'imaged' by the cameras (Table 1).
  • Pointing error bound P_as = Grid from 1 to 10 degrees
    Uniform noise up to plus or minus P_as is added to right ascension and declination in Eq. (10); the entire error vs. pointing-accuracy map is generated by sweeping this value.
  • Required detection effectiveness P_o = 85%
    The genetic algorithm is asked to meet P_success >= 85% with 90% of satellites operational; the resulting 44-satellite design is a consequence of these targets.
  • Operational percentage P_op = 90%
    In the outage model, 10% of the T satellites are randomly defunct during each Monte Carlo meteor event (Eq. 16), which directly drives the constellation size needed to still see each event with 3 spacecraft.
assumptions (6)
  • domain assumption The meteor trail is a straight line segment of finite length in the atmosphere.
    Simulated meteors are generated as straight lines of length l_M (Section 3, 'the meteor trail is generated by drawing a line of length l_M in an arbitrary direction'); real meteor trails can be curved or fragmented.
  • domain assumption Spacecraft cameras are perfectly calibrated so each pixel maps to an inertial right ascension and declination.
    Section 3, beginning of localization: 'We assume that the spacecraft cameras are calibrated, which means that each pixel inside the FoV of the camera corresponds to right ascension and declination angles measured in an inertial reference frame.'
  • domain assumption Spacecraft positions in the inertial frame are known exactly when solving the localization linear system.
    The localization algorithm uses spacecraft coordinates (x_j, y_j, z_j) in Eqs. (6) and (9) without modeling errors in those positions; the Monte Carlo only adds noise to angles, not to spacecraft ephemeris.
  • standard math Ceplecha's closed-form plane coefficients (Eq. 5) provide the optimal plane fit for a set of pixel directions.
    Borrowed from Reference [6] (Ceplecha 1987) for photographic fireball networks; the paper relies on this external derivation without re-proving it.
  • domain assumption All spacecraft are nadir-pointing with a wide FoV that satisfies the minimum elevation angle constraint (Eq. 13).
    Constellation coverage is evaluated using this FoV model; pointing errors of the actual spacecraft are not included in the coverage calculation, only in the localization error study.
  • domain assumption A Walker-Delta pattern with elliptical orbits is an adequate architecture for the meteor monitoring constellation.
    Section 3, Constellation Design: 'The constellation geometry will be similar to a Walker-Delta constellation but will allow elliptical orbits.' No alternative architectures (e.g., distributed or responsive constellations) are compared.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Cooperative Multi-spacecraft Observation of Incoming Space Threats." pith.science (2026). https://pith.science/paper/FQVJRSBE

@misc{pith2026190901814,
  author       = {Pith},
  title        = {Pith review of: Cooperative Multi-spacecraft Observation of Incoming Space Threats},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FQVJRSBE}},
  note         = {Machine review of arXiv:1909.01814}
}
read the original abstract

Earth is constantly being bombarded with material from space. Most of the natural material end up being dust grains that litter the surface of Earth, but larger bodies are known to impact every few decades. The most recent large impact was Chelyabinsk which set off a 500-kiloton explosion which was 40 times that of the Hiroshima nuclear explosion. Apart from meteors, there is a growing threat of space assets deorbiting. With these impending space threats, it is critical to have a constellation of satellites to autonomously lookout for meteors and reentering space debris. By using multiple spacecraft, it is possible to perform multipoint observation of the event. Through multipoint observation, it is possible to triangulate the location of the observed event. The detection, tracking, and analysis of these objects all need to be performed autonomously. Our previous work focused on developing several vision algorithms including blob-detection, feature detection, and neural network-based image segment classification. For this multipoint observation to occur, it requires multiple spacecraft to coordinate their actions particularly fixating on the space observation target. Furthermore, communication and coordination are needed for bringing new satellites into observation view and removing other satellites that have lost their view. In this paper, we analyze state-of-the-art observation technology for small satellites and perform detailed design of its implementation. Through this study, we estimate the error estimates on position, velocity, and acceleration. We presume use of low to mid-tier cameras for the spacecraft.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

32 extracted references · 32 canonical work pages

  1. [1]

    These are caused when meteoroids, the ejected fragments of asteroids or comets, enter the Earth’s atmosphere

    INTRODUCTION Meteor showers are common events that occur throughout the year. These are caused when meteoroids, the ejected fragments of asteroids or comets, enter the Earth’s atmosphere. Most of these fragments are small and end up as dust grains on the surface of Earth, and therefore are harmless. However, the larger bodies that release massive amounts ...

    work page 2013

  2. [2]

    These events have been observed at altitudes ranging from 70 − 140 km and where the entry velocities range from 11 − 72 km/s

    RELATED WORK Most meteor showers have been known to be caused by objects whose diameter is greater than 2 mm. These events have been observed at altitudes ranging from 70 − 140 km and where the entry velocities range from 11 − 72 km/s. Photometric observations of these meteors have been shown to provide plenty of insight into the entering body. If a singl...

    work page

  3. [3]

    We begin by providing a brief derivation of the localization algorithm, and then describe a validation study to test these algorithms

    METHODOLOGY This section describes the methodology used in the current work to design meteor monitoring constellation . We begin by providing a brief derivation of the localization algorithm, and then describe a validation study to test these algorithms. We then present the constellation design as an optimization problem when subjected to real-world const...

    work page

  4. [4]

    Accuracy of localization: The parameters used in the for studying the accuracy of the localization algorithm are presented in Table 1

    RESULTS & DISCUSSION This section presents the results of the accuracy estimation simulations and followed by the design of an optimal constellation which meets a user-defined success criterion. Accuracy of localization: The parameters used in the for studying the accuracy of the localization algorithm are presented in Table 1. The distribution of the mea...

    work page

  5. [5]

    CONCLUSION In this work, we developed novel algorithms and simulation architectures for constellations intended to detect meteor entry events. We began by proposing that a space- based visual meteor monitoring network is more effective than ground-based networks due to limited FoV of ground observers and atmospheric interferences. We then derived the mete...

    work page

  6. [6]

    Fireball and Bolide Data

    “Fireball and Bolide Data.” NASA, NASA, cneos.jpl.nasa.gov/fireballs

    work page

  7. [7]

    Chelyabinsk Airburst, Damage Assessment, Meteorite Recovery, and Characterization

    Popova, Olga P., et al. "Chelyabinsk Airburst, Damage Assessment, Meteorite Recovery, and Characterization." Science 342.6162 (2013): 1069-1073

    work page 2013

  8. [8]

    The Kessler Syndrome: Implications to Future Space Operations,

    Kessler, D.J., Johnson, N.L., Liou, J.C. and Matney, M., 2010. “The Kessler Syndrome: Implications to Future Space Operations,” Advances in the Astronautical Sciences, 137(8), p.2010

    work page 2010

Show all 32 references
  1. [9]

    Meteors in the Earth's Atmosphere: Meteoroids and Cosmic Dust and Their Interactions with the Earth's Upper Atmosphere,

    Murad, E. and Williams, I.P. eds., 2002, “Meteors in the Earth's Atmosphere: Meteoroids and Cosmic Dust and Their Interactions with the Earth's Upper Atmosphere,” Cambridge University Press

    2002

  2. [10]

    and Nassar, S., 2001

    Schwarz, K.P. and Nassar, S., 2001. “A simple algorithm for bridging DGPS outages by INS bias modeling, “ In Proc., 3rd Int. Symp. on Mobile Mapping Technology

    2001

  3. [11]

    “Geometric, dynamic, orbital and photometric data on meteoroids from photographic fireball networks, “ Bulletin of the Astronomical Institutes of Czechoslovakia, 38, pp.222-234

    Ceplecha, Z., 1987. “Geometric, dynamic, orbital and photometric data on meteoroids from photographic fireball networks, “ Bulletin of the Astronomical Institutes of Czechoslovakia, 38, pp.222-234

    1987

  4. [12]

    The status of the NASA all sky fireball network,

    Cooke, W.J. and Moser, D.E., 2011. “The status of the NASA all sky fireball network,”

    2011

  5. [13]

    The Southern Ontario all-sky meteor camera network,

    Weryk, R.J., Brown, P.G., Domokos, A., Edwards, W.N., Krzeminski, Z., Nudds, S.H. and Welch, D.L., 2007. “The Southern Ontario all-sky meteor camera network,” In Advances in Meteoroid and Meteor Science (pp. 241-246). Springer, New York, NY

    2007

  6. [14]

    A comprehensive list of meteor showers obtained from 10 years of observations with the IMO Video Meteor Network,

    Molau, S. and Rendtel, J., 2009. “A comprehensive list of meteor showers obtained from 10 years of observations with the IMO Video Meteor Network,” WGN, Journal of the International Meteor Organization, 37, pp.98-121

    2009

  7. [15]

    Global positioning systems, inertial navigation, and integration,

    Grewal, M.S., Weill, L.R. and Andrews, A.P., 2007. “Global positioning systems, inertial navigation, and integration,” John Wiley & Sons

    2007

  8. [16]

    Systems engineering trades for the iridium constellation,

    Garrison, T.P., Ince, M., Pizzicaroli, J. and Swan, P.A., 1997. “Systems engineering trades for the iridium constellation,” Journal of Spacecraft and Rockets, 34(5), pp.675-680

    1997

  9. [17]

    The CloudSat mission and the A- Train: A new dimension of space-based observations of clouds and precipitation,

    Stephens, G.L., Vane, D.G., Boain, R.J., Mace, G.G., Sassen, K., Wang, Z., Illingworth, A.J., O'Connor, E.J., Rossow, W.B., Durden, S.L. and Miller, S.D., 2002. “The CloudSat mission and the A- Train: A new dimension of space-based observations of clouds and precipitation,” Bu...

    2002

  10. [18]

    Flower constellation set theory. Part I: Compatibility and phasing,

    Mortari, D. and Wilkins, M.P., 2008. “Flower constellation set theory. Part I: Compatibility and phasing,” IEEE Transactions on Aerospace and Electronic Systems, 44(3), pp.953-962

    2008

  11. [19]

    A common-period four-satellite continuous global coverage constellation,

    Draim, J.E., 1987. “A common-period four-satellite continuous global coverage constellation,” Journal of Guidance, Control, and Dynamics, 10(5), pp.492-499

    1987

  12. [20]

    Mission geometry: orbit and constellation design and management: spacecraft orbit and attitude systems

    Wertz, James Richard. "Mission geometry: orbit and constellation design and management: spacecraft orbit and attitude systems." Mission geometry: orbit and constellation design and management: spacecraft orbit and attitude systems/James R. Wertz. El Segundo, CA; Boston: Microc...

    2001

  13. [21]

    Space Mission Engineering: The New SMAD,

    Wertz, J.R., Everett, D.F. and Puschell, J.J., 2011. “Space Mission Engineering: The New SMAD,” Microcosm Press

    2011

  14. [22]

    & McClain, W.D., 2013

    Vallado, D.A. & McClain, W.D., 2013. Fundamentals of astrodynamics and applications 4th ed., Hawthorne, CA: Microcosm Press

    2013

  15. [23]

    SWIMSat: Space Weather and Meteor Impact Monitoring using a Low-Cost 6U CubeSat,

    Hernandez, V., Gankidi, P., Chandra, A., Miller, A., Scowen, P., Barnaby, H., Adamson, E., Asphaug, E. and Thangavelautham, J., 2016. “SWIMSat: Space Weather and Meteor Impact Monitoring using a Low-Cost 6U CubeSat,” Proc. of the Small Satellite Conference

    2016

  16. [24]

    FPGA architecture for deep learning and its application to planetary robotics,

    Gankidi, P.R. and Thangavelautham, J., 2017, “FPGA architecture for deep learning and its application to planetary robotics,” In 2017 IEEE Aerospace Conference (pp. 1-9). IEEE

    2017

  17. [25]

    Vision based tracking controller for an on-orbit meteor observer,

    Nallapu, R., Ravindran, A. and Thangavelautham, J., “Vision based tracking controller for an on-orbit meteor observer,” 41st AAS Guidance and Control Conference, 2018

    2018

  18. [26]

    Smart camera system onboard a CubeSat for space-based object reentry and tracking

    Nallapu, R., Ravindran, A. and Thangavelautham, J., "Smart camera system onboard a CubeSat for space-based object reentry and tracking." Position, Location, and Navigation Symposium (PLANS), 2018 IEEE/ION. IEEE, 2018

    2018

  19. [27]

    Meteors in the Earth's Atmosphere: Meteoroids and Cosmic Dust and Their Interactions with the Earth's Upper Atmosphere,

    Murad, Edmond, and Iwan P. Williams, eds. “Meteors in the Earth's Atmosphere: Meteoroids and Cosmic Dust and Their Interactions with the Earth's Upper Atmosphere,” Cambridge University Press, 2002

    2002

  20. [28]

    On-Orbit Meteor Impact Monitoring Using CubeSat Swarms,

    Nallapu, R., Kalita, H. and Thangavelautham, J., 2018. “On-Orbit Meteor Impact Monitoring Using CubeSat Swarms,” 41st AAS Guidance and Control Conference, 2018

    2018

  21. [29]

    Attitude Control of Spacecraft Swarms for Visual Mapping of Planetary Bodies,

    Nallapu, R. and Thangavelautham, J., 2019, “Attitude Control of Spacecraft Swarms for Visual Mapping of Planetary Bodies,” In 2019 IEEE Aerospace Conference (pp. 1-16). IEEE

    2019

  22. [30]

    A globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds,

    Conn, A.R., Gould, N.I. and Toint, P., 1991. “A globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds,” SIAM Journal on Numerical Analysis, 28(2), pp.545-572

    1991

  23. [31]

    Asteroid Regolith Mechanics and Primary Accretion Experiments in a Cubesat,

    Asphaug, E. and Thangavelautham, J., 2014, “Asteroid Regolith Mechanics and Primary Accretion Experiments in a Cubesat,” Proceedings of the 45th Lunar and Planetary Science Conference

    2014

  24. [32]

    Asteroid Origins Satellite 1: An On-orbit CubeSat Centrifuge Science Laboratory,

    Lightholder, J., Thoesen, A., Adamson, E., Jakubowski, J., Nallapu, R., Smallwood, S., Raura, L., Klesh, A., Asphaug, E. and Thangavelautham, J., “Asteroid Origins Satellite 1: An On-orbit CubeSat Centrifuge Science Laboratory,” Acta Astronautica, Vol 133, 2017, pp. 81-94

    2017

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.