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We used attitude data from the Mars Ingenuity helicopter with a simple steady-state model to estimate wind speeds and directions at altitudes between 3 and 24 m, the first time winds at such altitudes have been probed on Mars. We compared our estimates to wind data from the meteorology package MEDA on board the Mars 2020 Perseverance rover and to predictions from meteorological models. Wind directions inferred from Ingenuity data agreed with the directions measured by MEDA, when the latter were available, but deviated from model-predicted directions by as much as 180° in some cases. The inferred wind speeds are often much higher than expected. For example, meteorological predictions suggest that Ingenuity should not have seen wind speeds above about 15 m s −1 during its 59th flight, but we inferred speeds reaching nearly 25 m s −1 . For flights during which we have MEDA data to compare to, inferred wind speeds imply friction velocities >1 m s −1 and roughness lengths >10 cm, which seem implausibly large. These results suggest that Ingenuity was probing winds sensitive to aerodynamic conditions hundreds of meters upwind instead of the conditions very near Mars 2020, but they may also reflect a need for updated boundary layer wind models. An improved model for Ingenuity’s aerodynamic response that includes the effects of transient winds may also modify our results. In any case, the work here provides a foundation for exploration of planetary boundary layers using drones and suggests important future avenues for research and development.

This study provides a single-point position estimation technique for interplanetary missions by observing visible planets using star trackers. Closed-form least-squares solution is obtained by minimizing the sum of the expected object-space squared distance errors. A weighted least-squares solution is provided by an iterative procedure. The weights are evaluated using the distances to the planets estimated by the least-squares solution. It is shown that the weighted approach only requires one iteration to converge and results in significant accuracy gains compared to simple least squares approach. The light-time correction is taken into account while the star-light aberration cannot be implemented in single-point estimation as it requires knowledge of the observer velocity. The proposed method is numerically validated through a statistical scenario as follows. A three-dimensional grid of test cases is generated: two dimensions sweep through the ecliptic plane and the third dimension sweeps through time from January 1, 2018 to January 1, 2043 in 5-year increments. The observer position is estimated at each test case and the estimate error is recorded. The results obtained show that a large majority of positions are well suited to position estimation by using star trackers pointing to visible planets, and reliable and accurate single-point position estimations can be provided in interplanetary missions. The proposed approach is suitable to be used to initiate a filtering technique to increase the estimation accuracy.

This paper presents a new method to estimate position from line-ofsight measurements to known targets when attitude is known. The algorithm has two stages. The first produces a closed-form unbiased estimate for position that does not account for the measurement error covariance. The second stage is iterative and produces an estimate of position that explicitly accounts for the measurement error covariance and the coupling between measurement error and sensor-to-target distance. The algorithm gives an accurate estimate of both position and the corresponding position error covariance and has a low computational cost. The computational complexity is O(n) for n point-targets and only a 3 × 3 linear system must be solved. The algorithm is demonstrated for single-point position estimation to verify the accuracy of the resulting position and covariance. Significant improvements over current methods are shown through statistical tests. The algorithm is then demonstrated in the context of sequential filtering for space vehicle docking.

Vision-aided navigation is the process of fusing data from visual cameras with other information sources to provide vehicle state estimation. Fusing information from multiple sources in a statistically optimal manner requires accurate stochastic models of each information source. Developing such a model for visual measurements presents a number of challenges. Vision-aided navigation systems rely on a set of computer vision methods known as feature detection and tracking to abstract visual camera images into a data source amenable to state estimation. It is nearly universally assumed that the measurements produced by these methods have independent and identically distributed (IID) errors. This study presents evidence that directly contradicts these assumptions. Novel models for visual measurements that eliminate the IID assumption are developed. Estimators are designed around the models and tested. Results demonstrate a significant performance advantage over existing methods and also reveal new challenges and paradoxes that motivate further research. In addition to improving vision-aided navigation models, a set of flexible and robust data-driven estimation techniques are developed and demonstrated on both canonical problems and problems in vision-aided navigation.

We used attitude data from the Mars Ingenuity helicopter with a simple steady-state model to estimate windspeeds and directions at altitudes of 3 meters up to 24 meters, the first time winds at such altitudes have been probed on Mars. We compared our estimates to concurrent wind data at 1.5 m height from the meteorology package MEDA onboard the Mars 2020 Perseverance rover and to predictions from meteorological models. Wind directions inferred from the Ingenuity data agreed to within uncertainties with the directions measured by MEDA, when the latter were available, but deviated from model-predicted directions by as much as 180 deg in some cases. Also, the inferred windspeeds are often much higher than expected. For example, meteorological predictions tailored to the time and location of Ingenuity's 59th flight suggest Ingenuity should not have seen windspeeds above about 15 m/s, but we inferred speeds reaching nearly 25 m/s. By contrast, the 61st flight was at a similar time and season and showed weaker winds then the 59th flight, suggesting winds shaped by transient phenomena. For flights during which we have MEDA data to compare to, inferred windspeeds imply friction velocities exceeding 1 m/s and roughness lengths of more than 10 cm based on a boundary layer model that incorporates convective instability, which seem implausibly large. These results suggest Ingenuity was probing winds sensitive to aerodynamic conditions hundreds of meters upwind instead of the conditions very near Mars 2020, but they may also reflect a need for updated boundary layer wind models. An improved model for Ingenuity's aerodynamic response that includes the effects of transient winds may also modify our results. In any case, the work here provides a foundation for exploration of planetary boundary layers using drones and suggests important future avenues for research and development.