论文标题
使用向量观察的态度确定和估计：审查，挑战和比较结果
Attitude Determination and Estimation using Vector Observations: Review, Challenges and Comparative Results
论文作者
论文摘要
本文涉及态度确定和估计的问题。早期应用考虑了态度确定的代数方法。态度确定算法被高斯态度估计过滤器所取代（该态度估算过滤器仍在商业应用中广泛使用）。但是，高斯态度过滤器对测量噪声的敏感性促使非线性态度过滤器引入了态度动力学问题的非线性性质，并允许更简单的过滤器推导。本文介绍了几种类型的态度确定和估计算法的调查。每个类别均以连续和离散形式的文献示例进行详细说明和说明。通过模拟结果，通过瞬态和稳态误差来证明这些算法之间的比较。通过对稳态中每种算法的误差相关平均值，无穷大规范和标准偏差的统计分析来补充比较。 Keywords: Comparative Study, Attitude, Determination, Estimation, Filter, Adaptive Filter, Gaussian Filter, Nonlinear Filter, Overview, Review, Rodrigues Vector, Special Orthogonal Group, Unit-quaternion, Angle-axis, Determinstic, Stochastic, Continuous, Discrete, Multiplicative extended kalman filter, KF, EKF, MEKF, white noise, colored noise.
This paper concerns the problem of attitude determination and estimation. The early applications considered algebraic methods of attitude determination. Attitude determination algorithms were supplanted by the Gaussian attitude estimation filters (which continue to be widely used in commercial applications). However, the sensitivity of the Gaussian attitude filter to the measurement noise prompted the introduction of the nonlinear attitude filters which account for the nonlinear nature of the attitude dynamics problem and allow for a simpler filter derivation. This paper presents a survey of several types of attitude determination and estimation algorithms. Each category is detailed and illustrated with literature examples in both continuous and discrete form. A comparison between these algorithms is demonstrated in terms of transient and steady-state error through simulation results. The comparison is supplemented by statistical analysis of the error-related mean, infinity norm, and standard deviation of each algorithm in the steady-state. Keywords: Comparative Study, Attitude, Determination, Estimation, Filter, Adaptive Filter, Gaussian Filter, Nonlinear Filter, Overview, Review, Rodrigues Vector, Special Orthogonal Group, Unit-quaternion, Angle-axis, Determinstic, Stochastic, Continuous, Discrete, Multiplicative extended kalman filter, KF, EKF, MEKF, white noise, colored noise.
