A Close Examination of Multiple Model Adaptive Estimation Vs Extended Kalman Filter for Precision Attitude Determination

Quang M. Lam, John L. Crassidis
2013 AIAA Guidance, Navigation, and Control (GNC) Conference   unpublished
With today's advanced nonlinear filtering (e.g., Multiple Model Adaptive Estimator, MMAE, or Particle Filter, PF) and noise identification for filter update at the roll-off level (via the Q matrix) and noise cancellation at the measurement level approach a reasonable technology maturation level, it is strongly believed that an extremely high precision attitude determination can be practically achieved using commercial low cost low grade MEMS inertial sensors (i.e., MEMS gyros and accelerometers
more » ... and/or MEMS IMU). This paper revisits the MMAE design developed in [2] & [7] with a close examination of its performance using high fidelity models of the gyros and star tracker to determine its viability for a possible design and implementation of a new attitude determination system using low cost low grade MEMS gyros (i.e., > 0.035 deg/hr^0.5 and 3deg/hr bias) and CMOS Star Trackers (i.e., >70 arcseconds). The proposed MMAE design with gyros noise identification (i.e., Angular Random Walk (ARW) and Rate Random Walk (RRW) are primary elements to be estimated in real time for update and cancellation) is evaluated against the single EKF based design for a performance measure of how well the proposed MMAE and Noise Identification scheme improves over the baseline design. The design is evaluated using the Matlab based environment.
doi:10.2514/6.2013-5175 fatcat:mg3bcgilrzhifjf7qicm466yvm