A recursive algorithm of error covariance matrix of moving horizon estimation 滚动时域估计中先验估计误差协方差阵的递归算法
A sub - optimal kalman filter is presented in chapter 3 , and the relative error covariance matrix ( recm ) is introduced to evaluate the performance of the fusion process ; 3 给出一种多传感器分布式次优kalman滤波器,并以相对误差协方差矩阵作为量化指标,对该滤波器的融合效果进行评估; 3
Based on the multi - scale representation theory , we present a reduced order model for the solving of the inverse problem . also the relative error covariance matrix is used to analyze the performance of models with different orders ; 4 基于小波多尺度表示理论给出逆问题求解的多尺度降阶模型,同时用相对误差协方差矩阵对阶数不同的降阶模型的估计精度进行分析; 4
Based on the method expressing the positional uncertainty of point entities in surveying and mapping , uncertainty ellipse was employed to characterize the uncertainty region for the error dispersal of 2d target ' s position , and uncertainty ellipsoid for the 3d target . 1 - uncertainty ellipse was derived from positional error covariance 借鉴测绘学科中衡量点元位置不确定性的方法,分别用椭圆和椭球来表示二维目标和三维目标的位置误差散布的不确定性区域。
And then facing the problem of the channel estimation of the adaptive modulation system , we conclude out the channel estimation algorithms on maximum likelihood ( ml ) estimation and maximum a posteriori ( map ) estimation under the condition of flat fading channel and selective fading channel in detail . to meet flat fading channel , we simulate the relationship of the ratio between the error covariance in map estimation and ml estimation and pilot symbol message length . the conclusion can be drawn from these results 接着,对自适应调制系统中的信道估计问题难点,详细推导了平衰落信道条件下和选择性衰落信道条件下最大似然( ml )估计和最大后验概率( map )估计算法,针对平衰落信道,我们仿真了map估计和ml估计的方差与导频符号长度的关系,仿真结果表明,错误方差受多谱勒频率的变化影响最大,并且对实际的自适应调制系统,导频符号长度的取值超过20个符号长度时, map信道估计明显优于ml信道估计。
This filter is a combination of adaptive ud decomposition kalman filter with quad method . it use quad method to detect and correct the gross errors in observations , use ud decomposition technique to improve computation precision and overcome the instability of filter caused by instability of values , when divergence of kalman filter had been detected , an adaptive filter is employed to adjust the prediction error covariance matrix 该法用拟准检定法准确地探测和修正量测方程中存在的粗差;用ud分解算法改进了计算精度,克服了由于数值不稳定带来滤波的不稳定性;当判断滤波器发散后,则启用sage自适应滤波器,调整预测误差方差,以克服滤波器的发散。
Furthermore , utilizing the characteristic that filtering error covariance expresses filtering precision and the principle of information conservation , the dynamic and reasonable distribution of distributed tracks weight coefficient is accomplished . jerk model and strong tracking filter is organically assembled , and based on spatio - temporal synthetically analysis and lme , a self - learning estimation method of the system measurement variance is given . the method improves obviously the 3 、将jerk模型与强跟踪滤波算法有机地结合,并利用时空综合分析和极大似然估计的思想推导出了一种系统量测方差自学习修正方法,以优化强跟踪滤波算法中次优渐消因子和滤波增益的在线选择,同时根据多传感器数据融合具有改善滤波精度的性质,进而给出一种基于jerk模型的多传感器数据融合算法。
However , the performance of these algorithms that are based on linear approximation degrades considerably in highly nonlinear situation . whereas the ekf requires the evaluation of the jacobian to obtain the observation matrix , the cmkf needs it to compute the measurement error covariance . both of them employ linear approximation , and thus linear error is inevitible 论文第四章分析了ekf和cmkf在某些情况下跟踪性能不理想的原因: ekf需要对量测方程进行线性近似, cmkf在计算转换测量值误差的均值和方差时同样要进行线性近似,因此无法避免线性化误差。