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中文摘要：
Cubature卡尔曼滤波器（CKF）在非高斯噪声或统计特性未知时滤波精度将会下降甚至发散,为此提出了统计回归估计的鲁棒CKF算法。推导出线性化近似回归和直接非线性回归的鲁棒CKF算法,直接非线性回归克服了观测方程线性化近似带来的不足。具有混合高斯噪声的仿真实例比较了3种Cubature卡尔曼滤波器的滤波性能,结果表明这两种鲁棒CKF滤波精度及估计一致性明显优于CKF,直接非线性回归的CKF的鲁棒性更强,滤波性能更好。
英文摘要：
A class of robust Cubature Kalman filter（CKF） algorithm with statistical regression is proposed to solve the problem that the conventional CKF declines in accuracy and further diverges when the noise is not Gaussian noise or its prior statistic is unknown. Two kinds of robust CKFs with linear approximation regression or not are deduced and filtering steps are designed. The directly nonlinear regression overcomes the shortcoming of CKF with linear approximation of measurement align. Simulation example with a model of mixed Gaussian noise analyzes and contrasts the performances of filter with the three kinds of Cubature Kalman Filter. The results show that the two robust Cubature Kalman filters outbalance the conventional CKF in the accuracy and consistency of filtering, and the robust CKF without linear approximation owns stronger robustness and better performance compared with the other robust CKF.
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