Reconstruction of Atmospheric Properties from Mars Science Laboratory Entry, Descent, and Landing

Chen, Allen; Cianciolo, Alicia Dwyer; Vasavada, A. R.; Karlgaard, Chris; Barnes, J. R.; Cantor, B. A.; Kass, D. M.; Rafkin, Scot; Tyler, D.

doi:10.2514/1.a32708

Cited by 27 publications

(15 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are no instruments on InSight to directly measure the atmosphere during entry and descent. However, the deceleration profile was used to reconstruct the density (temperature and pressure) profile as has been done on previous missions (Blanchard & Desai, 2011; Chen et al, 2014; Magalhaes et al, 1999; Withers & Smith, 2006). The reconstruction (Karlgaard et al, 2020) compared well to the climatological background as well as the atmosphere models.…”

Section: Atmospherementioning

confidence: 99%

Assessment of InSight Landing Site Predictions

et al. 2020

View full text Add to dashboard Cite

Comprehensive analysis of remote sensing data used to select the Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) landing site correctly predicted the atmospheric temperature and pressure profile during entry and descent, the safe landing surface, and the geologic setting of the site. The smooth plains upon which the InSight landing site is located were accurately predicted to be generally similar to the Mars Exploration Rover Spirit landing site with relatively low rock abundance, low slopes, and a moderately dusty surface with a 3–10 m impact fragmented regolith over Hesperian to Early Amazonian basaltic lava flows. The deceleration profile and surface pressure encountered by the spacecraft during entry, descent, and landing compared well (within 1σ) of the envelope of modeled temperature profiles and the expected surface pressure. Orbital estimates of thermal inertia are similar to surface radiometer measurements, and materials at the surface are dominated by poorly consolidated sand as expected. Thin coatings of bright atmospheric dust on the surface were as indicated by orbital albedo and dust cover index measurements. Orbital estimates of rock abundance from shadow measurements in high‐resolution images and thermal differencing indicated very low rock abundance and surface counts show 1–4% area covered by rocks. Slopes at 100 to 5 m length scale measured from orbital topographic and radar data correctly indicated a surface comparably smooth and flat as the two smoothest landing sites (Opportunity and Phoenix). Thermal inertia and radar data indicated the surface would be load bearing as found.

show abstract

Section: Atmospherementioning

confidence: 99%

Assessment of InSight Landing Site Predictions

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Other in situ measurements of upper atmospheric neutral densities and temperatures were made prior to the arrival of MAVEN by the accelerometers on Viking Lander 1 and 2 (Seiff, ; Seiff & Kirk, , ; Withers et al, ); the Mars Pathfinder Atmospheric Structure Instrument and accelerometers during descent (Magalhaes et al, ; Schofield, ; Seiff et al, ; Spencer et al, ; Withers, Towner, et al, ); and the Mars Global Surveyor (MGS), Mars Odyssey (ODY), and Mars Reconnaissance Orbiter (MRO) accelerometers during the aerobraking phases of their missions (Bougher, Keating, et al, ; Keating et al, ; Tolson et al, , ; Tolson, Keating, Cancro, et al, ; Tolson, Keating, Noll, et al, ; Withers, ; Withers, Bougher, & Keating, ). The Phoenix lander, Mars Exploration Rovers Spirit and Opportunity, Beagle 2, and Mars Science Laboratory also collected atmospheric density and temperature data during their descents to the surface, but these measurements either do not extend above 120 km or are uncertain above this altitude (Blanchard & Desai, ; Chen et al, ; Holstein‐Rathlou et al, ; Karlgaard et al, ; Montabone et al, ; Withers & Catling, ; Withers & Smith, ). The Indian Space Research Organisation Mars Orbiter Mission (MOM; see Arunan & Satish, ) arrived at Mars subsequent to MAVEN, and the Mars Exospheric Neutral Composition Analyzer (MENCA; see Bhardwaj et al, ) quadrupole mass spectrometer on board MOM has collected in situ measurements of the Martian exosphere.…”

Section: Introductionmentioning

confidence: 99%

Thermal Structure of the Martian Upper Atmosphere From MAVEN NGIMS

et al. 2018

View full text Add to dashboard Cite

The Neutral Gas and Ion Mass Spectrometer (NGIMS) aboard the NASA Mars Atmosphere and Volatile EvolutioN (MAVEN) mission measures the structure and variability of the Martian upper atmosphere. We use NGIMS density profiles to derive upper atmospheric temperature profiles and investigate the thermal structure of this region. The thermal structure of the upper atmosphere is a critical component of understanding atmospheric loss to space, the main science objective of MAVEN, and measured temperatures serve as inputs to and constraints on photochemical and global circulation models. We describe proper treatment of the NGIMS data and correct for the horizontal motion of the spacecraft. Temperature profiles from week-long, low-altitude excursions executed by MAVEN, called Deep Dips, are used to investigate the diurnal variation of the temperature and the thermospheric gradient, which varies between 1.33 ± 0.16 and 2.69 ± 0.33 K km −1 on the dayside. NGIMS measurements acquired on nominal MAVEN orbits over more than a Martian year further elucidate the diurnal and latitudinal variations of the temperature. Diurnal variations of about a factor of 2, from 127 ± 8 to 260 ± 7 K, are observed high in the exosphere, and latitudinal variations of 39 ± 17 K are observed in this region. Comparisons indicate broad agreement between temperatures derived from MAVEN NGIMS with previous in situ and remote sensing observations of upper atmospheric temperatures. NGIMS temperatures are also shown to be consistent with predictions of a 1-D model which includes solar UV and near IR heating, thermal conduction, and radiative cooling in the CO 2 2 15-μm band.

show abstract

“…For the MSL entry trajectory, the north and east winds are essentially pure cross and tail winds, respectively. These estimates are reasonable given the atmospheric uncertainties, 24 and moreover support circumstantial evidence for winds based on the vehicle entry guidance response 31 and trajectory. 32 The wind estimates are also consistent with post-flight full state Kalman filter/smoother results from Ref.…”

Section: A Flight Reconstructionmentioning

confidence: 70%

Coupled Inertial Navigation and Flush Air Data Sensing Algorithm for Atmosphere Estimation

Karlgaard

Kutty

Schoenenberger

2015

AIAA Atmospheric Flight Mechanics Conference

View full text Add to dashboard Cite

This paper describes an algorithm for atmospheric state estimation that is based on a coupling between inertial navigation and flush air data sensing pressure measurements. In this approach, the full navigation state is used in the atmospheric estimation algorithm along with the pressure measurements and a model of the surface pressure distribution to directly estimate atmospheric winds and density using a nonlinear weighted least-squares algorithm. The approach uses a highfidelity model of atmosphere stored in table-look-up form, along with simplified models of that are propagated along the trajectory within the algorithm to provide prior estimates and covariances to aid the air data state solution. Thus, the method is essentially a reduced-order Kalman filter in which the inertial states are taken from the navigation solution and atmospheric states are estimated in the filter. The algorithm is applied to data from the Mars Science Laboratory entry, descent, and landing from August 2012. Reasonable estimates of the atmosphere and winds are produced by the algorithm. The observability of winds along the trajectory are examined using an index based on the discrete-time observability Gramian and the pressure measurement sensitivity matrix. The results indicate that bank reversals are responsible for adding information content to the system. The algorithm is then applied to the design of the pressure measurement system for the Mars 2020 mission. The pressure port layout is optimized to maximize the observability of atmospheric states along the trajectory. Linear covariance analysis is performed to assess estimator performance for a given pressure measurement uncertainty. The results indicate that the new tightly-coupled estimator can produce enhanced estimates of atmospheric states when compared with existing algorithms. Nomenclature C = Backward smoothing gain F = Linearization of f with respect to x f = Low-fidelity atmospheric model equations of motion G = Linearization of f with respect to u g = Gravitational acceleration, m/s 2 H = Linearization of h with respect to x h = Pressure distribution model, Pa I = Identity matrix J = Linearization of h with respect to u k = Integer time index N = Integer time index of final pressure measurement P = Covariance of x after the measurement model update = Static pressure, Pa p = Pressure measurement vector, Pa Q = Process noise spectral densitỹ Q = Process noise covariance R = Pressure measurement covariance matrix R = Pressure measurement covariance matrix augmented with navigation uncertainty R = Planetary radius, m = Specific gas constant, J/kg-K S = Prior covariance of x from low fidelity model T = Prior covariance of x from high fidelity model T = Atmospheric temperature, K u = Vehicle inertial state v n , v e , v d = Vehicle planet-relative north, east, and down velocity components, m/s W o = Discrete-time observability Gramian w n , w e , w d = North, east, and down wind velocity components, m/s X 11 , X 12 , X 22 = Van Loan integral sub-matrices x = Atmospheric sta...

show abstract

Reconstruction of Atmospheric Properties from Mars Science Laboratory Entry, Descent, and Landing

Cited by 27 publications

References 12 publications

Assessment of InSight Landing Site Predictions

Assessment of InSight Landing Site Predictions

Thermal Structure of the Martian Upper Atmosphere From MAVEN NGIMS

Coupled Inertial Navigation and Flush Air Data Sensing Algorithm for Atmosphere Estimation

Contact Info

Product

Resources

About