# Landing-Ellipse Contraction as a Technology Learning Curve: Quantifying the Precision-Landing Improvement Rate Across Mars Missions

**Candidate:** JPL_AUTONOMY_EDL_06
**Program:** COLLEGIUM 1st Battalion
**NORTH STAR / JPL category:** Entry, Descent, and Landing Systems
**Methodological anchors (Hall of Shoulders):** Joel Mokyr (economic history of technology; propositional versus prescriptive knowledge); Simon Kuznets (cliometric measurement; boundary, valuation, and netting discipline)
**Date:** 2026-06-15

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## Abstract

The precision with which spacecraft can be delivered to the Martian surface has improved by more than two orders of magnitude across the robotic exploration era. The Viking landers of 1976 were targeted to landing ellipses on the order of hundreds of kilometers in their major dimension; the Mars 2020 Perseverance rover was delivered inside an effective targeting region of a few kilometers. This dissertation asks whether that improvement follows a measurable learning curve, the empirical regularity from the economics of technology in which a performance metric improves at a roughly constant proportional rate as a function of cumulative experience or successive technology generations. The contribution is a single falsifiable proposition: the three-sigma landing-ellipse semi-major axis, equivalently the ellipse area, for Mars surface missions contracts along an exponential learning rate driven primarily by the insertion of onboard entry-descent-and-landing (EDL) guidance technologies, specifically guided (lifting) entry, the range-to-go parachute trigger, and terrain-relative navigation (TRN), rather than by improvements in launch-vehicle injection accuracy. The null hypothesis is that ellipse contraction is unrelated to onboard EDL-guidance technology generation.

The method is a log-linear learning-curve regression of landing-ellipse area on mission sequence and a set of EDL-guidance technology covariates, estimated across the United States-led Mars surface missions from Viking 1 through Mars 2020, with supporting reconstruction data drawn from the NASA Technical Reports Server, the Planetary Data System, and TechPort. Following Kuznets, the work treats the landing-ellipse series as a constructed measurement whose definitional boundary, valuation convention, and decomposition must be stated before any inference is drawn. Following Mokyr, it frames the technology covariates as discrete additions to the prescriptive knowledge base that make the landing technique self-correcting and extensible. Identification does not rest on asymptotic significance, which a sample of nine to eleven missions cannot support, but on two design features the historical record happens to contain: the InSight counterfactual, a late mission that deliberately flew an unguided ballistic entry into a large ellipse and so decouples technology generation from calendar date, and the approach-accuracy control, which holds constant the part of delivery accuracy attributable to launch and interplanetary navigation.

This document is a design-stage prospectus for a design-stage dissertation. The model, identification strategy, and pre-registered analysis plan are complete, and the data sources are named and accessible, but the regression has not yet been executed on the assembled dataset. Every numerical result is illustrative and explicitly labeled as such; no estimate is reported as an empirical finding. The strongest claim the executed design can support is a signed, order-of-magnitude, counterfactual-surviving attribution rather than a precise point estimate of a learning rate, and the design is built to be informative whichever way the central coefficient falls. The work matters to NASA and JPL because a defensible learning rate, attributed to specific technologies, converts qualitative claims about EDL maturity into a quantitative basis for setting landing-accuracy requirements and for valuing candidate guidance investments in the human-Mars and Mars Sample Return architectures now under study.

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## 1. The Single Falsifiable Contribution

The contribution is one testable proposition, stated verbatim as the competing hypotheses fixed at the design stage. These are reproduced exactly and are not paraphrased.

- **H1 (the contribution):** The three-sigma landing-ellipse semi-major axis (equivalently, ellipse area) for Mars surface missions declines along an exponential learning curve in which the dominant explanatory variables are onboard EDL-guidance technology generations (guided entry, range trigger, and terrain-relative navigation), and in which launch-vehicle and interplanetary injection accuracy, once the standard approach-navigation corrections are accounted for, is not the binding constraint on ellipse size.

- **H0 (the null):** Ellipse contraction is unrelated to onboard EDL-guidance technology generation. Under H0 the technology covariates carry no explanatory power once mission sequence or a time trend is included, and any apparent learning curve is either an artifact of a generic time trend or is driven by approach-navigation improvements rather than onboard guidance.

The proposition is falsifiable in the strict sense. H1 predicts a specific sign and a specific ordering of effects: that the gamma coefficients on the three technology indicators are jointly significant and negative, that the delta coefficient on the approach-accuracy control is small and insignificant, and, in the sharper Mokyrian reading, that the largest fit increments arrive at the guided-entry and TRN transitions with a smaller increment at the range trigger. A regression on the assembled data can return coefficients that contradict each of these predictions. If the technology-indicator coefficients are insignificant or wrongly signed, or if the approach-accuracy proxy absorbs their explanatory power, or if the InSight observation sits on rather than above the trend, H1 is rejected and H0 is not. The design is built so that the contradiction, if it comes, is the headline finding rather than a buried negative result.

It must be said plainly what the contribution is not. It is not a claim that the strong form of the learning curve, a constant percentage improvement per doubling of a large cumulative production count, holds for Mars landings. Mars landings violate the conditions under which that strong form is reliable, because there are fewer than a dozen of them and each is a bespoke vehicle rather than a unit off a production line. The defensible claim is weaker and is stated as such throughout: that ordering the missions by guidance generation produces a monotone, roughly log-linear contraction whose steps align with identifiable technology insertions. The falsifiable content lies in the alignment of the steps with the technologies, which the data can confirm or deny regardless of the label.

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## 2. The Problem and the Gap

Every Mars surface mission is committed, years before launch, to a landing ellipse: a probabilistic region on the surface, conventionally reported at three standard deviations, inside which the vehicle is expected to touch down. The size of the ellipse propagates into nearly every downstream decision a project makes. It determines which candidate sites can be considered, how much scientifically interesting terrain must be sacrificed to the safety constraint, the mass and complexity budget allocated to the EDL system, and the residual risk the project carries to its review boards.

The history of Mars surface exploration is, in large part, the history of shrinking that ellipse. The Viking landers of 1976 were targeted to regions whose major dimension was on the order of hundreds of kilometers. The Mars Exploration Rovers in 2004, still flying an essentially ballistic Pathfinder-heritage entry, landed inside ellipses on the order of a hundred kilometers. The Mars Science Laboratory in 2012 introduced guided lifting entry and a range-triggered parachute deploy and cut the targeting region to roughly twenty kilometers. The Mars 2020 mission added terrain-relative navigation and an autonomous divert, effectively delivering Perseverance to an effective targeting region of a few kilometers inside the hazardous Jezero crater site, a site that earlier systems could not have attempted at all. Across the era, the targeting capability improved by more than two orders of magnitude.

The problem is not the existence of the contraction but its causal ambiguity. The current state is that the cross-mission contraction is documented qualitatively, one mission at a time, with no joint time-series model and no formal attribution of the contraction to specific technologies against rival causes. Each engineering paper does its job superbly within its scope, reporting the design ellipse and the reconstructed performance for its own mission; none was designed to arbitrate causes across missions. The desired state is a single constructed ellipse series, fitted with a learning-rate model, that attributes the contraction to identifiable technology insertions while controlling for the alternative explanation that the vehicles are simply being delivered to the top of the atmosphere more accurately. The gap is that no published study joins the EDL-engineering literature to the technology-economics learning-curve apparatus, and none arbitrates among the rival causes of the contraction. The consequence is concrete: future architectures, the Mars Sample Return retrieval and the eventual human landing, must each specify a landing-accuracy requirement years in advance, and they currently do so by qualitative negotiation rather than by reading a defensible rate off an attributed curve, and they cannot value a guidance investment against an approach-navigation investment on a common quantitative basis.

Three things changed together, and monotonically, across the robotic era: the vehicles acquired more capable onboard guidance, the agency learned to model the entry environment with greater fidelity, and the approach navigation that delivers the vehicle to the atmospheric interface improved. Any one of these could explain a shrinking ellipse, and because the engineering papers each examine a single mission, they hold none of the others fixed. Setting up the comparison the literature has not, in a form where the data can settle it, is the contribution.

The EDL-engineering canon documents each technology lever in depth but never as one series and never with formal attribution against the injection-accuracy rival. Guided lifting entry and the entry-guidance algorithms are characterized for MSL precision landing; the range-to-go parachute trigger and its footprint reduction are documented for MSL; terrain-relative navigation and the Lander Vision System are reported for Mars 2020, with the onboard reference map built from orbital imagery and a relevant-environment flight test preceding the flight; hazard detection and avoidance, supersonic retropropulsion, and high-mass human-class EDL define the forward edge. Each technology lever maps to a physically distinct error source: guided entry nulls the hypersonic downrange and crossrange dispersion, the range trigger corrects the parachute-deploy dispersion, and TRN collapses the position-knowledge error and enables the divert. The technology-economics learning-curve and cliometric-measurement traditions, which could quantify and attribute the contraction, have simply never been brought into contact with that engineering canon. The contraction sits in one literature, and the apparatus that could attribute it sits in the other.

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## 3. Theoretical and Hall-of-Shoulders Anchors

The two anchor methodologists are load-bearing rather than decorative.

**Joel Mokyr** distinguishes propositional knowledge (the understanding of why a technique works) from prescriptive knowledge (the technique itself). The three EDL levers are treated as discrete additions to the prescriptive landing-technique base, each resting on a maturing propositional base, and the distinction between macro-invention and incremental improvement generates the design decision to model discrete technology generations as covariates rather than a featureless time trend. In the sharper Mokyrian reading, the largest fit increments should arrive at the macro-invention transitions (guided entry, TRN) and a smaller one at the more incremental range trigger.

**Simon Kuznets** insists that a constructed aggregate is meaningless without a stated boundary of coverage, a stated valuation convention, and a stated netting rule, and that a change must be decomposed before it is theorized. This generates the design decision to construct the ellipse series, with every confidence-level normalization and convention conversion recorded, before any slope is fitted, and the transient-versus-secular distinction that the InSight counterfactual is built to adjudicate: NASA forecasting culture is prone to extrapolating a one-time level shift as if it were a secular trend, and the design tests which the contraction is.

The conceptual model the empirical work tests is therefore technology-generation steps on a measurement-disciplined log series. The bridge from firm-level learning curves to a small-n, generation-indexed aerospace series runs through the organizational-learning literature on tacit versus codified knowledge.

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## 4. Method

The method is a log-linear ordinary-least-squares learning-curve regression. The dependent variable is `ln(EllipseArea_i)`, the natural log of the three-sigma landing-ellipse area, computed as ln(pi times a times b) using the design ellipse reported in the mission's EDL performance study. The log form is chosen for three reasons, not for convenience: it makes a slope coefficient a constant proportional contraction rate, the native unit of the experience-curve literature; it stabilizes variance across a series spanning more than two orders of magnitude so that each generation carries comparable weight in the fit; and it matches the physical mechanism, in which each lever removes a fraction of a distinct error budget rather than a fixed number of kilometers.

**Baseline specification:**

ln(EllipseArea_i) = beta_0 + beta_1 times Sequence_i + epsilon_i.

**Augmented specification:**

ln(EllipseArea_i) = beta_0 + beta_1 times Sequence_i + gamma_1 times GuidedEntry_i + gamma_2 times RangeTrigger_i + gamma_3 times TRN_i + delta times ApproachAccuracy_i + epsilon_i.

The learning-rate reading is exp(beta_1) minus one, the proportional change in ellipse area per unit of mission sequence. The baseline is the descriptive foil; the contribution lives in the contrast between the gamma coefficients (onboard guidance) and delta (approach accuracy) in the augmented specification.

Inference is permutation-based and exact rather than asymptotic, because with nine to eleven observations no other inference is honest. A linear-in-levels specification is carried as a robustness check, alongside the program-level experience unit that collapses the near-identical Viking and Mer pairs, a with-and-without-Viking sensitivity, and the cross-check of the design ellipse against the achieved miss distance.

**Identification** does not come from sample size or asymptotic significance. It comes from two design features physically and historically prior to any regression. The **InSight counterfactual**: InSight (2018) is a late mission that deliberately flew a Phoenix-heritage ballistic entry into a large flat Elysium Planitia ellipse, so it sits at a high sequence index with low technology indicators and is the one observation whose technology generation is decoupled from its date, breaking the otherwise perfect time-technology collinearity. The **approach-accuracy control**: the reconstructed delivered-entry-state dispersion holds constant the part of accuracy that launch and interplanetary navigation supply, so whatever contraction loads on the technology indicators after this control is in cannot be the injection-accuracy story (H0).

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## 5. Named Datasets

The analysis draws on three named, real, open NASA data sources, each requiring no credential.

1. **NTRS landing-accuracy reconstructions (the dependent-variable source).** The NASA Technical Reports Server (ntrs.nasa.gov), queryable through its public citations API at ntrs.nasa.gov/api/citations/search, hosts the post-flight statistical reconstructions of the design landing ellipse and reconstructed performance. The canonical statistical-reconstruction methodology of Karlgaard and colleagues was applied mission by mission: MSL, Mars 2020 (carried by the MEDLI2 suite), InSight, Phoenix, and Pathfinder. The earliest missions are where provenance thins, and the Viking-era values rest on 1970s simulation conventions that are flagged and carried with an explicit provenance band rather than treated as exact.

2. **TechPort technology-insertion records (the independent-variable source).** TechPort (techport.nasa.gov), NASA's public technology-portfolio database, establishes the mission on which each guidance technology first flew, which is the single fact each binary indicator encodes (GuidedEntry set to one for MSL and Mars 2020; RangeTrigger set to one for MSL and Mars 2020; TRN set to one for Mars 2020 only). Each coding is double-sourced, corroborated against the flight-reconstruction literature so no indicator rests on the portfolio record alone.

3. **PDS landing-site localization (the construct-validity safeguard).** The Planetary Data System (pds.nasa.gov) supplies the secondary dependent variable, the achieved miss distance between the targeted aim point and the localized landed position, computed from orbital imagery and cartography. Because it shares none of the Monte Carlo dispersion modeling, atmosphere assumptions, or confidence-level conventions of the design ellipse, agreement between the two series validates the construct, and divergence is itself reported as a finding rather than suppressed.

The unit of analysis is the mission landing event. The population is the entire relevant census of United States-led Mars surface missions that successfully completed EDL: Viking 1, Viking 2, Mars Pathfinder, MER Spirit, MER Opportunity, Phoenix, MSL/Curiosity, InSight, and Mars 2020/Perseverance (nine to eleven events depending on whether the within-program pairs are counted singly or doubly). Tianwen-1 (2021) is held out as an external-validity reference and is not in-sample.

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## 6. Significance, Scope, and Falsification

**Significance.** The result, conditional on execution, runs along three stakeholder lines. Requirements-setting: a coefficient on a technology indicator is, under H1, an estimate of the proportional ellipse reduction that flying that technology buys, which is exactly the input a requirement-setting trade needs and currently lacks for the Human Mars EDL architecture studies and Mars Sample Return. Investment valuation: the contrast between the gamma coefficients and delta values guidance against approach navigation on a common quantitative basis, a tool for prioritizing the constrained technology-development budget across NASA centers. Honest extrapolation: following Kuznets, the InSight counterfactual separates a genuine secular improvement trend from a one-time level shift, protecting a future project from reading a precision off a curve the underlying knowledge chain may not support.

**Scope and delimitations.** The fit is on robotic Mars landings only. Lunar precision-landing navigation and human-class EDL appear as forward and out-of-sample reference points, not in-sample evidence. The dependent variable is landing precision as captured by the three-sigma design ellipse, with achieved miss distance as a parallel construct-validity check; the work does not address landing safety, hazard density, or scientific yield except insofar as the ellipse governs which sites are reachable. The architecture-traceability layer of the underlying argument framework is deliberately out of scope: the contribution is an econometric and cliometric measurement-and-attribution claim about a constructed performance series, not the design of a real capability or system, and the decision relevance is carried in plain prose rather than as an architecture artifact.

**What would falsify the contribution.** H1 is rejected, and H0 not, if the technology-indicator coefficients are insignificant or wrongly signed, if the approach-accuracy proxy absorbs their explanatory power, or if the InSight observation sits on rather than above the trend. The central residual risks, irreducible small-n, simulation-convention drift, and near-perfect time-technology collinearity, are real but bounded: census coverage removes sampling bias, provenance recording and the with-and-without-Viking sensitivity bound the convention drift, and the InSight counterfactual and approach-accuracy control are built into the data to break the collinearity. No small-sample design can deliver a precise point estimate, and none is claimed; the design is built to make a contradicting result equally informative.

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## 7. Threats to Validity and the Pre-Registered Robustness Battery

Each classical validity threat is paired with the specific design feature that mitigates it, and no mitigation is claimed complete where it is not.

**Internal validity.** The dominant threat is the time-technology collinearity: any unobserved factor that improved monotonically across the era (modeling fidelity, atmospheric knowledge, flight-computer throughput, institutional learning) could load on the technology indicators and masquerade as a technology effect. The InSight counterfactual holds the calendar roughly fixed at the modern era while turning the guidance off, so a confound that tracks only the calendar cannot explain InSight's large ellipse; the approach-accuracy control removes the specific monotone confound, delivery accuracy, that is the most plausible rival cause. Confidence: moderate, rising with a second technology-off mission and falling if the achieved-miss-distance series fails to show the contraction the design ellipse shows.

**External validity.** The model is fit on United States robotic Mars landings only. Generalization is deliberately bounded rather than over-claimed: Tianwen-1 is held out and used only as an out-of-sample non-United-States reference; lunar autonomous precision-landing navigation is cited to establish that TRN is a general planetary capability rather than a Mars-specific artifact; and the human-scale precision-landing assessments are treated as the forward edge of the capability, not as observations the curve is fit to. Confidence beyond the estimation population: low, and the work says so.

**Construct validity.** The three-sigma design ellipse is a pre-flight simulation product whose conventions drifted across missions, so a reported contraction could partly reflect a change in how the ellipse was computed. The achieved miss distance from PDS localization is the independent realized-capability proxy; the full analysis is re-run on it, and agreement is the construct-validity test. Every ellipse value carries its sigma level and simulation-convention provenance, per the Kuznetsian boundary discipline.

**Statistical-conclusion validity.** With nine to eleven observations and five candidate slope parameters, conventional asymptotic inference is unreliable, so the design uses exact and permutation-based tests that build the reference distribution by reassigning the technology labels across missions, comparing the achieved value against the full distribution of relabelings rather than against a normal approximation. Effect sizes are reported with honestly wide permutation intervals.

**The pre-registered robustness battery.** Four re-fits are committed in advance and reported whether or not convenient: re-estimation on the achieved-miss-distance dependent variable (construct check); a drop-InSight re-fit that quantifies exactly how much of the conclusion the single counterfactual carries; the with-and-without-Viking sensitivity that exposes the dependence of the headline rate on the least-comparable observations; and a linear-in-levels re-fit against the log specification, with any disagreement in the sign or ordering of the technology terms reported as a finding rather than suppressed. A result that holds only in the primary specification but collapses across the battery is reported as fragile, not as confirmed.

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## 8. Analysis Plan: Decision Rule and Expected Signs

This is a design-stage analysis plan. The model and identification are complete and the data are accessible, but no regression has been run on the assembled dataset. Every numerical statement below is illustrative and labeled as such.

**The fixed decision rule (binding, stated before any estimate).** Support for H1 requires both: the technology coefficients gamma_1, gamma_2, gamma_3 are jointly significant under the permutation test and each carries a negative sign; and the approach-accuracy coefficient delta is small in magnitude and statistically insignificant. That conjunction, a negative jointly significant technology block with an insignificant approach control, is the precise signature the onboard-guidance mechanism predicts and the injection-accuracy rival does not. Failure to reject H0 follows from either: the technology terms are jointly insignificant once the sequence trend is included (generic maturation), or delta is significant and absorbs the technology block (approach navigation is the binding lever). Both null configurations are themselves decision-relevant and redirect where a future project should spend to shrink an ellipse. For the configuration the small sample makes most likely, a technology block correctly signed but not jointly significant, the verdict is "consistent with but not confirming H1," and the InSight residual is given decisive weight: above the trend yields qualified support, on the trend yields an honest non-result.

**Expected signs, each with a mechanism.** Guided lifting entry (gamma_1): negative, large, observed at MSL, nulling the hypersonic downrange and crossrange dispersion; in Mokyr's terms a macro-invention that changed the entry regime from ballistic to lifting. The range-to-go trigger (gamma_2): negative, smallest, also at MSL, attacking only the parachute-deploy dispersion; incremental, and the hardest to isolate because it shares its mission with guided entry, so the MSL increment is treated as a guided-entry-plus-range-trigger bundle whose within-bundle apportionment is the weakest inference in the study. Terrain-relative navigation (gamma_3): negative, largest single increment, observed at Mars 2020, attacking the position-knowledge error itself and enabling the autonomous divert; the most cleanly identified lever because Mars 2020 is the only mission carrying it. The approach-accuracy control (delta): expected small and insignificant under H1, because the thesis is that onboard guidance, not injection accuracy, is the binding constraint; this coefficient is the hinge of the whole test. The composite expected pattern is a large negative baseline sequence coefficient, the largest incremental fits at the MSL and Mars 2020 transitions, a negative jointly significant technology block, a small insignificant approach control, and an InSight observation sitting above the fitted trend. Any departure is informative, and several departures would falsify the contribution outright.

**The InSight reading.** The single most consequential reading is the position of the InSight observation relative to the fitted trend, interpreted as a one-case event-study profile. A large positive residual is evidence for the technology hypothesis and against generic maturation; a near-zero residual is evidence that time alone explains the contraction. The case can establish the discrimination between the technology and pure-time hypotheses, but it cannot apportion credit among the three levers, because InSight lacks all three at once and is a single point. The plan treats the residual as the linchpin of the technology-versus-time discrimination and explicitly declines to use it for within-block apportionment, with the drop-InSight re-fit quantifying exactly how much of the conclusion depends on it.

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## Seed Reference List

Drawn entirely from the dissertation's own corpus. Seed references (S01 to S24) first, then selected harvested references (R-series). Every entry is real and carries a resolvable DOI or NTRS citation URL.

1. Steltzner, A., et al. (2007). "Mars Science Laboratory: Entry, Descent, and Landing System Performance." *IEEE Aerospace Conference*. doi: [10.1109/aero.2007.352821](https://doi.org/10.1109/aero.2007.352821)
2. Way, D. W., et al. (2008). "Mars Science Laboratory: Entry, Descent, and Landing System Overview." *IEEE Aerospace Conference*. doi: [10.1109/aero.2008.4526283](https://doi.org/10.1109/aero.2008.4526283)
3. Mendeck, G. F., et al. (2008). "Entry Guidance Performance for Mars Precision Landing." *Journal of Guidance, Control, and Dynamics* 31(4). doi: [10.2514/1.36950](https://doi.org/10.2514/1.36950)
4. Mendeck, G. F., & Craig, L. (2011). "Entry Guidance for the 2011 Mars Science Laboratory Mission." *AIAA*. doi: [10.2514/6.2011-6639](https://doi.org/10.2514/6.2011-6639)
5. Way, D. W. (2011). "On the use of a range trigger for the Mars Science Laboratory Entry, Descent, and Landing." *IEEE Aerospace Conference*. doi: [10.1109/aero.2011.5747242](https://doi.org/10.1109/aero.2011.5747242)
6. Desai, P. N., et al. (2011). "Entry, Descent, and Landing Performance of the Mars Phoenix Lander." *Journal of Spacecraft and Rockets* 48(5). doi: [10.2514/1.48239](https://doi.org/10.2514/1.48239)
7. Karlgaard, C. D., et al. (2007). "Statistical Reconstruction of Mars Entry, Descent, and Landing Trajectories and Atmospheric Profiles." *AIAA*. doi: [10.2514/6.2007-6192](https://doi.org/10.2514/6.2007-6192)
8. Johnson, A. E., et al. (2022). "Mars 2020 Lander Vision System Flight Performance." *AIAA SciTech*. doi: [10.2514/6.2022-1214](https://doi.org/10.2514/6.2022-1214)
9. Cheng, Y., et al. (2021). "Making an Onboard Reference Map From MRO/CTX Imagery for Mars 2020 Lander Vision System." *Earth and Space Science* 8(4). doi: [10.1029/2020ea001560](https://doi.org/10.1029/2020ea001560)
10. Johnson, A. E., et al. (2015). "Real-Time Terrain Relative Navigation Test Results from a Relevant Environment for Mars Landing." *AIAA*. doi: [10.2514/6.2015-0851](https://doi.org/10.2514/6.2015-0851)
11. Wang, T., et al. (2022). "Planetary landings with terrain sensing and hazard avoidance: A review." *Advances in Space Research* 71(8). doi: [10.1016/j.asr.2022.11.024](https://doi.org/10.1016/j.asr.2022.11.024)
12. Karlgaard, C. D., et al. (2022). "Mars Entry, Descent, and Landing Instrumentation 2 Trajectory, Aerodynamics, and Atmosphere Reconstruction." *Journal of Spacecraft and Rockets* 59(4). doi: [10.2514/1.a35440](https://doi.org/10.2514/1.a35440)
13. (NASA/JPL) (2021). "Reconstruction of Entry, Descent, and Landing Communications for the InSight Mars Lander." *Journal of Spacecraft and Rockets* 58(4). doi: [10.2514/1.a34892](https://doi.org/10.2514/1.a34892)
14. Golombek, M., et al. (2016). "Selection of the InSight Landing Site." *Space Science Reviews* 211. doi: [10.1007/s11214-016-0321-9](https://doi.org/10.1007/s11214-016-0321-9)
15. Yu, Z., et al. (2021). "The Tianwen-1 Guidance, Navigation, and Control for Mars Entry, Descent, and Landing." *Space: Science & Technology* 2021. doi: [10.34133/2021/9846185](https://doi.org/10.34133/2021/9846185)
16. Alberth, S. (2008). "Forecasting technology costs via the experience curve, Myth or magic?" *Technological Forecasting and Social Change* 75(7). doi: [10.1016/j.techfore.2007.09.003](https://doi.org/10.1016/j.techfore.2007.09.003)
17. Ziegler, M. S., & Trancik, J. E. (2021). "Re-examining rates of lithium-ion battery technology improvement and cost decline." *Energy & Environmental Science* 14. doi: [10.1039/d0ee02681f](https://doi.org/10.1039/d0ee02681f)
18. Mokyr, J. (2002). *The Gifts of Athena: Historical Origins of the Knowledge Economy.* Princeton University Press. (Hall-of-Shoulders methodological anchor: propositional-versus-prescriptive knowledge and the macro-versus-incremental-invention distinction.)
19. Kuznets, S. (1941). *National Income and Its Composition, 1919-1938.* NBER. (Hall-of-Shoulders methodological anchor: boundary-valuation-netting requirement and the transient-versus-secular distinction.)
20. Karlgaard, C. D., Kutty, P., Schoenenberger, M., & Shidner, J. D. (2013). "Mars Science Laboratory Entry, Descent, and Landing Trajectory and Atmosphere Reconstruction." NASA Technical Reports Server. URL: [https://ntrs.nasa.gov/citations/20130010087](https://ntrs.nasa.gov/citations/20130010087)
21. Karlgaard, C. D., Korzun, A. M., Schoenenberger, M., & Bonfiglio, E. P. (2020). "Mars InSight Entry, Descent, and Landing Trajectory and Atmosphere Reconstruction." NASA Technical Reports Server. URL: [https://ntrs.nasa.gov/citations/20200002910](https://ntrs.nasa.gov/citations/20200002910)
22. Kallemeyn, P. H., Peng, C. Y., Braun, R. D., & Thurman, S. W. (1998). "Mars Pathfinder Atmospheric Entry Reconstruction." NASA Technical Reports Server. URL: [https://ntrs.nasa.gov/citations/20210005354](https://ntrs.nasa.gov/citations/20210005354)
23. Steltzner, A. D., San Martin, A. M., & Rivellini, T. P. (2015). "Mars Science Laboratory Entry, Descent and Landing System Development Challenges and Preliminary Flight Performance." NASA Technical Reports Server. URL: [https://ntrs.nasa.gov/citations/20150012005](https://ntrs.nasa.gov/citations/20150012005)
24. Martin-Mur, T. J., Kruizinga, G. L., Burkhart, P. D., & Wong, M. C. (2015). "Mars Science Laboratory Navigation Results." NASA Technical Reports Server. URL: [https://ntrs.nasa.gov/citations/20150005583](https://ntrs.nasa.gov/citations/20150005583)
25. Dutta, S., Way, D. W., Casoliva, J., & Brugarolas, P. (2021). "Mars 2020 Perseverance EDL GNC Safe Target Selection Reconstruction." NASA Technical Reports Server. URL: [https://ntrs.nasa.gov/citations/20230005646](https://ntrs.nasa.gov/citations/20230005646)