基于并行模型自适应滤波的空间目标相对位姿估计

摘要： 扩展卡尔曼滤波(EKF)的估计精度受限于测量噪声统计特性的准确程度，如果敏感器测量噪声方差偏离其标称值，将会对滤波性能产生不利影响.尽管自适应扩展卡尔曼滤波(AEKF)能够对测量噪声方差进行估计，但是，噪声特性准确的情况下，AEKF的性能往往不及传统EKF.针对上述问题，本文提出一种并行模型自适应滤波(PMAF)，基于特定的自适应率将EKF和AEKF结合起来，使得在先验信息准确的情况下，EKF在状态估计中起主导作用；相反，在实际噪声方差偏离标称值时，令AEKF起主导作用.这样，即能有效削弱测量噪声统计特性不确定性对滤波性能的影响，又能确保正常情况下的估计精度.以空间目标相对位姿估计为例，通过数学仿真对EKF、AEKF和PMAF进行了对比研究，表明所提算法的综合性能优于传统方法.

Space Target Relative Attitude and Position Estimation Based on Parallel Model Adaptive Filter

Abstract： The successful use of the standard extended Kalman filter (EKF) is restricted by the requirement on the statistics information of the measurement noise. The filtering performance may decline due to the statistical uncertainty. Although the adaptive extended Kalman filter (AEKF) is available for recursive covariance estimation, it is often less accurate than the EKF with accurate noise statistics. Aiming at this problem, this paper develops a parallel adaptive extended Kalman filter (PAEKF) by combining the EKF and the AEKF with an adaptive law, such that the final state estimate is dominated by the EKF when the prior noise covariance is accurate, while the AEKF is activated when the actual noise covariance deviates from its nominal value. The PAEKF can reduce the sensitivity of the algorithm to the model uncertainty, and ensure the estimation accuracy in the normal case. For spacecraft relative attitude and position estimation, the simulation results demonstrate that the PAEKF has the advantage of both the AEKF and the EKF.