量测不确定的四元数约束CKF姿态估计

针对姿态估计系统在量测不确定和四元数约束下存在发散及估计精度差的缺陷,提出了一种基于不确定量测的四元数约束容积卡尔曼滤波算法(quaternion constrained cubature Kalman filter based on uncertain measurements,UCCKF).该算法克服了约束容积卡尔曼滤波算法的局限性,采用独立的伯努利随机变量来描述量测的不确定性,利用三阶球面-相径容积规则近似计算非线性函数的后验均值和协方差.并针对四元数规范化问题,采用两步投影理论来解决四元数约束限制.仿真结果表明,相比较于约束容积卡尔曼滤波(constrained cubature Kalman filter,CCKF)和无迹混合滤波(unscented mixture filter,UMF),提出的UCCKF算法在量测不确定情况下具有更好的收敛性和更高的估计精度,说明该算法对量测不确定下的非线性姿态估计系统是有效、可行的.

Quaternion constrained cubature Kalman filter attitude estimation based on uncertain measurements

In terms of the uncertainty measurement and the constraint on the attitude quaternion, this paper proposes a quaternion constrained cubature Kalman filter based on uncertain measurements (UCCKF) for the spacecraft attitude estimation system. The proposed filter algorithm overcomes the limitation of the quaternion-constrained cubature Kalman filter. An independent Bernoulli random process is introduced to describe the measurement uncertainty, and the three degree spherical radial cubature rule is adopted to computer the posterior mean and covariance of the system state. To deal with quaternion normalization problem, the two-step projection theory is applied to solve the quaternion constraint. Simulation results show that, compared with constrained cubature Kalman filter and unscented mixture filter, the proposed UCCKF can help to implement the better convergence and higher estimation accuracy in the case of measurement uncertainty. This illustrates that the proposed algorithm is effective and feasible for the nonlinear attitude estimation system.