【摘要】 针对在星间相对导航中噪声的统计特性未知可能引起滤波估计精度下降甚至发散的问题，提出了一种自适应简化容积卡尔曼滤波(ASCKF)算法。将Sage-Husa自适应滤波与容积卡尔曼滤波(CKF)相结合，通过容积规则摆脱线性滤波的局限性。改进Sage-Husa噪声估计器以避免噪声方差在线估计可能出现的非正定现象，从而保证了滤波器对噪声统计变化的自适应能力。结合编队卫星运动模型的特点，用常规卡尔曼滤波(KF)的时间更新代替相应的容积变换过程，在不影响滤波器性能的前提下减少了运算量。仿真结果表明：在测量噪声统计特性未知的情况下，与CKF相比，该文算法对相对状态的估计精度提高了近25%,同时滤波器的稳定性也得到了提高。更多还原

【Abstract】 Aiming at the problem that the unknown system noise statistics in the relative navigation of satellite formations may lead to the decline or even divergence of estimation accuracy, an adaptive simplified cubature Kalman filter(ASCKF)is proposed. Sage-Husa adaptive filter is combined with cubature Kalman filter(CKF)to get rid of the limitations of linear filter through cubature rules. Sage-Husa noise estimator is improved to avoid possible nonpositive definite phenomena in online estimation of noise variance, ensuring the filter can adapt to the changes of noise statistics. Due to the characteristics of motion model, the time update of standard Kalman filter(KF)is used to replace the corresponding cubature transformation process, which reduces calculation without affecting the performance of the filter. The simulation results show that, in the case of unknown noise statistics, compared with CKF,the proposed algorithm improves the estimation accuracy of relative states by nearly 25%,and the stability of the filter is also improved.更多还原