Adaptive Particle Filtering for Spacecraft Attitude Estimation from Vector Observations
Adaptive Particle Filtering for Spacecraft Attitude Estimation from Vector Observations
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DOI：
10.2514/1.35878
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发表时间：
2009-01-01
影响因子：
2.6
通讯作者：
Oshman, Yaakov
中科院分区：
文献类型：
--
作者：
Carmi, Avishy;Oshman, Yaakov
对最近提出的遗传算法进行了扩展--嵌入式四元数粒子滤波。遗传算法嵌入的四元数粒子滤波器属于蒙特卡洛序贯方法类，是一种估计器，它使用近似的数值表示技术，用于在固有的非线性姿态估计问题中执行潜在的非高斯概率密度函数的精确时间传播和测量更新。航天器的姿态表示通过四元数的旋转，和一个遗传算法被用来估计陀螺仪的偏差，允许一个通过粒子滤波器估计的四元数。本文提出了一种自适应版本的遗传算法嵌入四元数粒子滤波器，扩展了该滤波器的适用性与高度不确定的测量噪声分布的问题。自适应算法估计飞行中的测量噪声分布，沿着航天器姿态和陀螺仪偏差。仿真研究是用来证明的自适应算法的性能，使用从Technion的TechSAT卫星，其三轴磁强计的数据是非高斯的真实的数据。仿真结果表明，该算法与非自适应遗传算法-嵌入式四元数粒子滤波算法相比，具有较好的滤波性能。
An extension is presented to the recently introduced genetic algorithm-embedded quaternion particle filter. Belonging to the class of Monte Carlo sequential methods, the genetic algorithm-embedded quaternion particle filter is an estimator that uses approximate numerical representation techniques for performing the otherwise exact time propagation and measurement update of potentially non-Gaussian probability density functions in the inherently nonlinear attitude estimation problem. The spacecraft attitude is represented via the quaternion of rotation, and a genetic algorithm is used to estimate the gyro biases, allowing one to estimate just the quaternion via the particle filter. An adaptive version of the genetic algorithm-embedded quaternion particle filter is presented herein that extends the applicability or this filter to problems with highly uncertain measurement noise distributions. The adaptive algorithm estimates the measurement noise distribution on the fly, along with the spacecraft attitude and gyro biases. A simulation study is used to demonstrate the performance of the adaptive algorithm using real data obtained from the Technion's TechSAT satellite, whose three-axis magnetometer's data are non-Gaussian. The simulation, which compares the performance of the filter to the nonadaptive genetic algorithm-embedded quaternion particle filter, demonstrates the viability of the new algorithm.