Extended Kalman Filter(EKF)algorithm is widely used in parameter estimation for nonlinear systems.The estimation precision is sensitively dependent on EKF’s initial state covariance matrix and state noise matrix.The ...Extended Kalman Filter(EKF)algorithm is widely used in parameter estimation for nonlinear systems.The estimation precision is sensitively dependent on EKF’s initial state covariance matrix and state noise matrix.The grid optimization method is always used to find proper initial matrix for off-line estimation.However,the grid method has the draw back being time consuming hence,coarse grid followed by a fine grid method is adopted.To further improve efficiency without the loss of estimation accuracy,we propose a genetic algorithm for the coarse grid optimization in this paper.It is recognized that the crossover rate and mutation rate are the main influencing factors for the performance of the genetic algorithm,so sensitivity experiments for these two factors are carried out and a set of genetic algorithm parameters with good adaptability were selected by testing with several gyros’experimental data.Experimental results show that the proposed algorithm has higher efficiency and better estimation accuracy than the traversing grid algorithm.展开更多
介绍了基于时间序列分析的陀螺仪随机误差模型的建立,并对误差模型进行滤波补偿的方法。主要是利用e vie w s软件将陀螺仪随机漂移数据进行平稳化处理,然后对处理后的时间序列进行模型的识别与定阶,最后结合kalm an滤波方法对建立随机...介绍了基于时间序列分析的陀螺仪随机误差模型的建立,并对误差模型进行滤波补偿的方法。主要是利用e vie w s软件将陀螺仪随机漂移数据进行平稳化处理,然后对处理后的时间序列进行模型的识别与定阶,最后结合kalm an滤波方法对建立随机误差模型滤波补偿。实验结果表明,该方法建立的模型很好地反映了陀螺仪随机漂移的趋势,并有效地抑制了陀螺仪的随机噪声,提高了其输出精度。展开更多
文摘Extended Kalman Filter(EKF)algorithm is widely used in parameter estimation for nonlinear systems.The estimation precision is sensitively dependent on EKF’s initial state covariance matrix and state noise matrix.The grid optimization method is always used to find proper initial matrix for off-line estimation.However,the grid method has the draw back being time consuming hence,coarse grid followed by a fine grid method is adopted.To further improve efficiency without the loss of estimation accuracy,we propose a genetic algorithm for the coarse grid optimization in this paper.It is recognized that the crossover rate and mutation rate are the main influencing factors for the performance of the genetic algorithm,so sensitivity experiments for these two factors are carried out and a set of genetic algorithm parameters with good adaptability were selected by testing with several gyros’experimental data.Experimental results show that the proposed algorithm has higher efficiency and better estimation accuracy than the traversing grid algorithm.
文摘介绍了基于时间序列分析的陀螺仪随机误差模型的建立,并对误差模型进行滤波补偿的方法。主要是利用e vie w s软件将陀螺仪随机漂移数据进行平稳化处理,然后对处理后的时间序列进行模型的识别与定阶,最后结合kalm an滤波方法对建立随机误差模型滤波补偿。实验结果表明,该方法建立的模型很好地反映了陀螺仪随机漂移的趋势,并有效地抑制了陀螺仪的随机噪声,提高了其输出精度。
