A novel low-cost adaptive square-root cubature Kalmanfilter （LCASCKF） is proposed to enhance the robustness of processmodels while only increasing the computational load slightly.It is well-known that the Kalman fil...A novel low-cost adaptive square-root cubature Kalmanfilter （LCASCKF） is proposed to enhance the robustness of processmodels while only increasing the computational load slightly.It is well-known that the Kalman filter cannot handle uncertainties ina process model, such as initial state estimation errors, parametermismatch and abrupt state changes. These uncertainties severelyaffect filter performance and may even provoke divergence. Astrong tracking filter （STF）, which utilizes a suboptimal fading factor,is an adaptive approach that is commonly adopted to solvethis problem. However, if the strong tracking SCKF （STSCKF）uses the same method as the extended Kalman filter （EKF） tointroduce the suboptimal fading factor, it greatly increases thecomputational load. To avoid this problem, a low-cost introductorymethod is proposed and a hypothesis testing theory is applied todetect uncertainties. The computational load analysis is performedby counting the total number of floating-point operations and it isfound that the computational load of LCASCKF is close to that ofSCKF. Experimental results prove that the LCASCKF performs aswell as STSCKF, while the increase in computational load is muchlower than STSCKF.展开更多
This paper presents a kind of attitude estimation algorithm based on quaternion-vector switching and square-root cubature Kalman filter for autonomous underwater vehicle(AUV).The filter formulation is based on geomagn...This paper presents a kind of attitude estimation algorithm based on quaternion-vector switching and square-root cubature Kalman filter for autonomous underwater vehicle(AUV).The filter formulation is based on geomagnetic field tensor measurement dependent on the attitude and a gyro-based model for attitude propagation. In this algorithm, switching between the quaternion and the three-component vector is done by a couple of the mathematical transformations. Quaternion is chosen as the state variable of attitude in the kinematics equation to time update, while the mean value and covariance of the quaternion are computed by the three-component vector to avoid the normalization constraint of quaternion. The square-root forms enjoy a continuous and improved numerical stability because all the resulting covariance matrices are guaranteed to stay positively semidefinite. The entire square-root cubature attitude estimation algorithm with quaternion-vector switching for the nonlinear equality constraint of quaternion is given. The numerical simulation of simultaneous swing motions in the three directions is performed to compare with the three kinds of filters and the results indicate that the proposed filter provides lower attitude estimation errors than the other two kinds of filters and a good convergence rate.展开更多
基于前后张驰逼近(Back and Forth Nudging,简称BFN)和集合卡尔曼滤波(En KF)方法,构建了一种新的同化方法 HBFNEn KF(Hybrid Back and Forth Nudging En KF)混合同化方法,并将此同化系统分别与通道浅水模式(shallow water model)和全...基于前后张驰逼近(Back and Forth Nudging,简称BFN)和集合卡尔曼滤波(En KF)方法,构建了一种新的同化方法 HBFNEn KF(Hybrid Back and Forth Nudging En KF)混合同化方法,并将此同化系统分别与通道浅水模式(shallow water model)和全球浅水模式对接,检验了HBFNEn KF同化方法的有效性。同时,对比了集合均方根滤波(En SRF)、HNEn KF(Hybrid Nudging En KF)、HBFNEn KF三种方法在有误差模式中的同化效果。试验结果表明:HBFNEn KF同化方法保留了HNEn KF方法的同化连续性,解决了En KF同化不连续不平滑的问题,同时还有着更快的收敛速度;当采用单变量分析试验时,HBFNEn KF方法的优势最为明显,表明HBFNEn KF能够较好地保持不同模式变量间的平衡。此外,增量场尺度分析结果表明:相比En SRF,HBFNEn KF在大尺度范围有更好的同化效果,同时能够避免在中小尺度范围内出现大量的虚假增量。展开更多
基金supported by the National Natural Science Foundation of China(61573283)
文摘A novel low-cost adaptive square-root cubature Kalmanfilter （LCASCKF） is proposed to enhance the robustness of processmodels while only increasing the computational load slightly.It is well-known that the Kalman filter cannot handle uncertainties ina process model, such as initial state estimation errors, parametermismatch and abrupt state changes. These uncertainties severelyaffect filter performance and may even provoke divergence. Astrong tracking filter （STF）, which utilizes a suboptimal fading factor,is an adaptive approach that is commonly adopted to solvethis problem. However, if the strong tracking SCKF （STSCKF）uses the same method as the extended Kalman filter （EKF） tointroduce the suboptimal fading factor, it greatly increases thecomputational load. To avoid this problem, a low-cost introductorymethod is proposed and a hypothesis testing theory is applied todetect uncertainties. The computational load analysis is performedby counting the total number of floating-point operations and it isfound that the computational load of LCASCKF is close to that ofSCKF. Experimental results prove that the LCASCKF performs aswell as STSCKF, while the increase in computational load is muchlower than STSCKF.
基金supported by the National Natural Science Foundation of China(1140503561004130+4 种基金60834005)the Natural Science Foundation of Heilongjiang Province of China(F201414)the Postdoctoral Scientific Research Developmental Fund of Heilongjiang Province(LBHQ15034)the Stable Supporting Fund of Acoustic Science and Technology Laboratory(JCKYS2019604SSJS002)the Fundamental Research Funds for the Central Universities。
文摘This paper presents a kind of attitude estimation algorithm based on quaternion-vector switching and square-root cubature Kalman filter for autonomous underwater vehicle(AUV).The filter formulation is based on geomagnetic field tensor measurement dependent on the attitude and a gyro-based model for attitude propagation. In this algorithm, switching between the quaternion and the three-component vector is done by a couple of the mathematical transformations. Quaternion is chosen as the state variable of attitude in the kinematics equation to time update, while the mean value and covariance of the quaternion are computed by the three-component vector to avoid the normalization constraint of quaternion. The square-root forms enjoy a continuous and improved numerical stability because all the resulting covariance matrices are guaranteed to stay positively semidefinite. The entire square-root cubature attitude estimation algorithm with quaternion-vector switching for the nonlinear equality constraint of quaternion is given. The numerical simulation of simultaneous swing motions in the three directions is performed to compare with the three kinds of filters and the results indicate that the proposed filter provides lower attitude estimation errors than the other two kinds of filters and a good convergence rate.
文摘基于前后张驰逼近(Back and Forth Nudging,简称BFN)和集合卡尔曼滤波(En KF)方法,构建了一种新的同化方法 HBFNEn KF(Hybrid Back and Forth Nudging En KF)混合同化方法,并将此同化系统分别与通道浅水模式(shallow water model)和全球浅水模式对接,检验了HBFNEn KF同化方法的有效性。同时,对比了集合均方根滤波(En SRF)、HNEn KF(Hybrid Nudging En KF)、HBFNEn KF三种方法在有误差模式中的同化效果。试验结果表明:HBFNEn KF同化方法保留了HNEn KF方法的同化连续性,解决了En KF同化不连续不平滑的问题,同时还有着更快的收敛速度;当采用单变量分析试验时,HBFNEn KF方法的优势最为明显,表明HBFNEn KF能够较好地保持不同模式变量间的平衡。此外,增量场尺度分析结果表明:相比En SRF,HBFNEn KF在大尺度范围有更好的同化效果,同时能够避免在中小尺度范围内出现大量的虚假增量。
