Linear Covariance Analysis for Powered Lunar Descent and Landing

Geller, David; Christensen, Daniel P.

doi:10.2514/1.38641

Cited by 52 publications

(21 citation statements)

References 8 publications

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“…The values for these parameters are displayed in Table 3.3. These values are the same as those used in the first stage of the ALHAT work [7] and are consistent with the DAC1 assumptions for sensors and navigation. The rate at which all of these sensors are sampled in the linear covariance simulation is 0.5 Hz.…”

Section: Baseline Sensorssupporting

confidence: 71%

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Terrain-Relative and Beacon-Relative Navigation for Lunar Powered Descent and Landing

Christensen

Geller

2011

J of Astronaut Sci

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As NASA prepares to return humans to the moon and establish a long-term presence on the surface, technologies must be developed to access previously unvisited terrain regardless of the condition. Among these technologies is a guidance, navigation and control (GNC) system capable of safely and precisely delivering a spacecraft, whether manned or robotic, to a predetermined landing area. This thesis presents detailed research of both terrain-relative navigation using a terrain-scanning instrument and beacon-relative radiometric navigation using beacons in lunar orbit or on the surface of the moon. The models for these sensors are developed along with a baseline sensor suite that includes an altimeter, IMU, velocimeter, and star camera. Linear covariance analysis is used to rapidly perform the trade studies relevant to this problem and to provide the navigation performance data necessary to determine which navigation method is best suited to support a 100 m 3-σ navigation requirement for landing anytime and anywhere on the moon.(90 pages) iv

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Section: Baseline Sensorssupporting

confidence: 71%

“…For the first phase of this analysis, the linear covariance simulation that was used in the previous ALHAT analysis [7] was modified to include a higher-fidelity model of the TRN sensor (see page 26). This simulation now has a total of 63 true states for the lander, the landing site, the sensors, and the environment.…”

Section: Linear Covariance Simulationmentioning

confidence: 99%

Terrain-Relative and Beacon-Relative Navigation for Lunar Powered Descent and Landing

Christensen

Geller

2011

J of Astronaut Sci

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“…[1][2][3] The attitude of a lunar lander is determined by propagating the gyro's angular velocity measurement, and the attitude observation provided by the star tracker is combined with the gyro by the Kalman filter to improve the attitude accuracy for all phase of descent and landing trajectory. The attitude errors affect the position and velocity errors because the non-gravitational acceleration measurement of accelerometer is transformed to the navigation reference frame using the direction cosine matrix calculated by the estimated attitude.…”

Section: Introductionmentioning

confidence: 99%

Autocovariance least-squares based measurement error covariance estimation for attitude determination of lunar lander

Park

2015

Proceedings of the Institution of Mechanical Engineers, Part G:

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In this paper, the autocovariance least-squares (ALS) method for a linear time-varying system is proposed for estimating both the process noise covariance and the measurement noise covariance associated with the measurement sensor to mitigate the performance degradation caused by an incorrect information of the sensor errors or by a large change of errors in sensor measurement. To verify the efficiency of the proposed method, simulations were performed for the attitude determination of the lunar lander which combines a gyro and a star tracker measurements assuming that the star tracker errors are increased by fault during the lunar descent and landing. The simulation results show that the attitude error of the proposed method is smaller than the conventional Kalman filter by adaptively tuning the noise covariance of the star tracker measurement errors. Also, the relationship between the ALS estimation accuracy and the innovation sample size and the time lag is discussed.

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“…However, the navigation and control errors of the actual close-looped autonomous rendezvous must differ from the fixed levels of navigation and control errors, and research into optimal multi-objective trajectory design based on close-looped control is necessary. The method of linear covariance analysis [13,14] was developed for the closelooped control system. This method has been used for the covariance analysis in missions such as autonomous rendezvous [13] and deep-space exploration [14], and the errors of dynamic, navigation, and control can be taken into account.…”

Section: Introductionmentioning

confidence: 99%

“…The method of linear covariance analysis [13,14] was developed for the closelooped control system. This method has been used for the covariance analysis in missions such as autonomous rendezvous [13] and deep-space exploration [14], and the errors of dynamic, navigation, and control can be taken into account. Compared with Monte Carlo covariance analysis, linear covariance analysis is much more efficient at obtaining the covariance of a system state, which makes optimal trajectory design based on close-looped control possible.…”

Section: Introductionmentioning

confidence: 99%

Optimal multi-objective trajectory design based on close-looped control for autonomous rendezvous

Tang

2011

Sci. China Technol. Sci.

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This paper considers the problem of optimal multi-objective trajectory design for autonomous rendezvous. Total velocity cost and relative state robustness of close-looped control are selected as the objective functions. Based on relative dynamics equations, the state equations and measurement equations for angles-only relative navigation between spacecrafts are set forth. According to the method of linear covariance analysis, the close-looped control covariance of the true relative state from the reference relative state is analyzed, and the objective functions of relative state robustness are formulated. Considering the total velocity cost and the relative state robustness, the multi-objective optimization algorithm of NSGA-II is employed to solve this multi-impulsive rendezvous problem. Lastly, the validity of the objective functions and the covariance results are demonstrated through 100 times Monte Carlo simulation. autonomous rendezvous, relative navigation, close-looped control, multi-objective, robustness Citation:Li J R, Li H Y, Tang G J. Optimal multi-objective trajectory design based on close-looped control for autonomous rendezvous.

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Linear Covariance Analysis for Powered Lunar Descent and Landing

Cited by 52 publications

References 8 publications

Terrain-Relative and Beacon-Relative Navigation for Lunar Powered Descent and Landing

Terrain-Relative and Beacon-Relative Navigation for Lunar Powered Descent and Landing

Autocovariance least-squares based measurement error covariance estimation for attitude determination of lunar lander

Optimal multi-objective trajectory design based on close-looped control for autonomous rendezvous

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