HCKF性能评估及其在传递对准中的应用

针对高阶容积卡尔曼滤波(HCKF)算法在传递对准中的应用问题，提出综合运用多变量函数泰勒级数展开和计算复杂度分析的性能评估方法。基于高阶球面-径向容积准则建立HCKF算法模型，并对其估计精度和计算量进行量化和对比分析：HCKF具有5阶泰勒级数展开精度且计算复杂度为O(n 4)，综合性能优于高斯厄米特积分滤波(GHQF)和无迹卡尔曼滤波(UKF)。将三种非线性滤波算法应用到舰机大失准角传递对准系统中，摇摆台试验结果表明：HCKF的估计精度比UKF高17%，计算量比GHQF小2个数量级，与HCKF性能评估结果一致。

Performance Evaluation of HCKF and Its Application in Transfer Alignment

Aiming at the applicability of the high-degree cubature Kalman filter (HCKF) in the transfer alignment, a performance evaluation method based on the Taylor series expansion of a multivariable function and the computational complexity analysis is proposed. The model of HCKF is established based on the high-order spherical-radial cubature rule, and the estimation accuracy and calculation amount are quantified and analyzed. The HCKF can capture the fifth-order terms of the Taylor series expansion, and the computational complexity is at least O(n 4). Moreover, the theoretical analysis also demonstrates that the comprehensive performance of the HCKF is better than the other two typical nonlinear filter algorithms, the Gaussian-Hermite quadrature filter (GHQF) and the unscented Kalman filter (UKF). The swing bench test results are achieved by applying the three non-linear filter algorithms to the transfer alignment system of the carrier-born aircraft under large misalignment angle. It shows that the estimation accuracy of HCKF is 17% higher than that of the UKF, and its calculation amount is two orders of magnitude smaller than that of the GHQF, which is consistent with the conclusion of the HCKF performance evaluation.