科技报告详细信息
Analytic Spacecraft Attitude and Rate Estimation Performance During Attitude Sensor Outages
Galante, Joseph M
关键词: ALGORITHMS;    ANALYSIS (MATHEMATICS);    ANGULAR VELOCITY;    ATTITUDE CONTROL;    ATTITUDE GYROS;    BIAS;    ERROR ANALYSIS;    FIELD OF VIEW;    GYROSCOPES;    KALMAN FILTERS;    MISSION PLANNING;    MONTE CARLO METHOD;    NOISE GENERATORS;    STAR DISTRIBUTION;    STAR TRACKERS;    STEADY STATE;    WHITE NOISE;   
RP-ID  :  GSFC-E-DAA-TN74144-2
学科分类:航空航天科学
美国|英语
来源: NASA Technical Reports Server
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【 摘 要 】
Analytic expressions for spacecraft attitude and rate estimation performance of an attitude estimation filter in terms of sensor specifications are useful tools for spacecraft design. Farrenkopf (1978) famously found analytic expressions for steady-state pre-update and post-update attitude and gyro bias estimate error variances for an attitude estimation filter for a single-axis spacecraft with a Rate Output Gyro (ROG). Markley and Reynolds (2000) extended the analysis for a Rate-Integrating Gyro (RIG) with angle white noise. These expressions allow for the rapid evaluation of system performance during preliminary mission design phases. One contribution of this paper is the analytic calculation of the steady-state pre-update and post-update angular rate estimate uncertainty for both the ROG and RIG cases. The primary contribution of this paper is the extension of the results for both the ROG and the RIG cases to the situation of an attitude sensor outage. This situation arises frequently in practice; for example when a star sensor’s field of view is occluded, when a star sensor’s readings are unreliable during a thruster burn that vibrates the spacecraft, or during star sensor outages due to radiation upsets. Analytic expressions for the attitude estimate uncertainty, gyro bias estimate uncertainty, and angular rate estimate uncertainty are given in terms of the attitude sensor outage interval, the star tracker measurement noise, and gyro noise parameters. Validity of the analytic results is demonstrated via Monte Carlo simulation.
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