Covariance Analysis of Maximum Likelihood Attitude Estimation1

نویسندگان

  • Joanna C. Hinks
  • John L. Crassidis
چکیده

An attitude determination covariance measurement model for unit vector sensors with a wide field-of-view is analyzed and compared to the classic QUEST covariance model. The wide field-of-view model has been previously proposed as a more realistic alternative for sensors where measurement accuracy depends on angular distance from the boresight axis. Both QUEST and the wide field-of-view models are evaluated relative to a measurement model that uses the two-dimensional sensor focal plane measurements directly, rather than first converting them to unit vectors. The Cramér-Rao lower bound is derived for attitude determination based on such direct sensor measurements, and the wide field-of-view measurement model is shown to achieve this Cramér-Rao lower bound. Numerical simulations confirm that an extended Kalman filter based on the wide field-of-view model outperforms a filter based on the QUEST model, and also that the wide field-of-view 3σ bounds are effectively identical to those of a filter based on the direct two-dimensional sensor measurements. INTRODUCTION One of the requirements of most spacecraft missions is attitude determination. In this process, some combination of sensor measurements is used to determine the orientation of a spacecraft with respect to some chosen reference frame. Some attitude determination methods process a batch of measurements that apply at a specific instant in time. In particular, many strategies have been developed to find the optimal attitude by minimizing the Wahba problem cost function [1]. Other techniques estimate the attitude history of a dynamically-rotating spacecraft by applying an extended Kalman filter (EKF) or other filtering algorithms [2, 3, 4, 5]. For a survey of approaches to attitude determination, see Ref. [6]. Common sensors for attitude determination include three-axis magnetometers, Sun sensors, Earth-horizon sensors, star trackers and onboard GPS receivers [7]. In the context of filtering, these are often combined with gyroscopic rate measurements. Attitude determination strategies, both static and dynamic, require accurate mathematical models that relate each distinct type of measurement to the spacecraft attitude. Measurement models have two components. First, 1This paper is dedicated to the memory of Dr. Malcolm D. Shuster and the Acme Spacecraft Company. 2Postdoctoral Assistant, Department of Mechanical & Aerospace Engineering, University at Buffalo, State University of New York, Amherst, NY, 14260-4400. Email: [email protected]. 3Professor, Department of Mechanical & Aerospace Engineering, University at Buffalo, State University of New York, Amherst, NY, 14260-4400. Email: [email protected].

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Analysis of Covariance Structures Under Elliptical Distributions

This article examines the adjustment of normal theory methods for the analysis of covariance structures to make them applicable under the class of elliptical distributions. It is shown that if the model satisfies a mild scale invariance condition and the data have an elliptical distribution, the asymptotic covariance matrix of sample covariances has a structure that results in the retention of ...

متن کامل

Covariance Tapering for Likelihood Based Estimation in Large Spatial Datasets

Maximum likelihood is an attractive method of estimating covariance parameters in spatial models based on Gaussian processes. However, calculating the likelihood can be computationally infeasible for large datasets, requiring O(n3) calculations for a dataset with n observations. This article proposes the method of covariance tapering to approximate the likelihood in this setting. In this approa...

متن کامل

The Maximum Likelihood Estimators in a Multivariate Normal Distribution with Ar(1) Covariance Structure for Monotone Data

The maximum likelihood estimators are uniquely obtained in a multivariate normal distribution with AR(1) covariance structure for monotone data. The maximum likelihood estimator of mean is unbiased.

متن کامل

Comparison of Local and Non-Local Methods in Covariance Matrix Estimation by Using Multi-baseline SAR Interferometry and Height Extraction for Principal Components with Maximum Likelihood Approach

By today, the technology of synthetic aperture radar (SAR) interferometry (InSAR) has been largely exploited in digital elevation model (DEM) generation and deformation mapping. Conventional InSAR technique exploits two SAR images acquired from slightly different angles, in which the information of elevation and deformation can be captured through processing of the phase difference of the image...

متن کامل

Sparsistency and Rates of Convergence in Large Covariance Matrix Estimation1 by Clifford Lam

This paper studies the sparsistency and rates of convergence for estimating sparse covariance and precision matrices based on penalized likelihood with nonconvex penalty functions. Here, sparsistency refers to the property that all parameters that are zero are actually estimated as zero with probability tending to one. Depending on the case of applications, sparsity priori may occur on the cova...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2012