【摘要】 针对噪声统计信息未知或时变情况下常规卡尔曼滤波估计精度下降甚至发散的问题,提出了一种基于极大似然估计的新息自适应滤波算法。算法对基于极大似然估计的常规新息协方差估值器进行限定记忆指数衰减加权修正,增加滑动窗口内新近新息协方差序列的利用权重;根据新息自适应原理,利用新息协方差估计值直接计算滤波增益矩阵,加快滤波器收敛速度的同时提高了滤波算法的估计精度。算法应用于捷联惯性导航系统/全球定位系统(SINS/GPS)组合导航系统,仿真实验表明:在噪声统计信息未知或时变情况下,算法具有更强的鲁棒性以及更高的滤波精度。更多还原

【Abstract】 Aiming at problem of decline or even the divergence of precision of conventional Kalman filtering estimation when noise statistics information are unknown or time-varying,an innovation adaptive filtering algorithm based on the maximum likelihood estimation is proposed. The maximum likelihood-based conventional innovation covariance estimator is corrected by the limited memory exponential attenuation weighting to increase usage weight of recent innovation covariance sequences in sliding window. According to innovation adaptive principle,calculate filtering gain matrix directly utilizing estimated value of innovation covariance,which promotes the convergence rate of filter and improves estimation precision of filtering algorithm at the same time. Simulations are performed in the strapdown inertial navigation system/global positioning system(SINS/GPS) integrated navigation system,and the results show that the proposed algorithm has stronger robust and higher filtering precision when the noise statistics information are unknown or time-varying.更多还原