低成本AHRS/GPS紧耦合融合滤波技术研究
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摘要
随着MEMS技术的发展,微型惯性测量组合(MIMU)在低成本捷联导航系统中得到广泛应用。航姿参考系统(AHRS)是一套由MIMU和地磁传感器组成的三自由度姿态测量单元,具有体积小、重量轻、高可靠和耐冲击等突出特性,可实现动态环境下载体实时姿态航向控制。将其与小型GPS组合起来,构成载体最理想的组合导航定位模式。
扩展卡尔曼滤波(EKF)是处理组合导航系统信息融合最有效的手段,针对MEMS传感器漂移大、稳定性差等特点,开展改进的EKF方法与神经网络智能控制相结合的融合滤波方法,以寻求最佳导航参数估计精度。论文围绕AHRS单机工作算法以及AHRS/GPS紧耦合融合滤波技术两部分开展研究工作。
在掌握各传感器工作机理的基础上,深入研究微机械AHRS捷联姿态算法,利用加速度计和磁通门传感器对重力、地磁两不共线参考向量的观测,修正陀螺积分后的角度漂移,建立载体系下姿态Kalman滤波器观测修正方程,对陀螺零漂进行有效估计和补偿。
从陆用载体姿态变化可能过快这一实际情况出发,阐明缩短载体姿态更新周期的必要性。引入对偶四元数(DQ)理论,以优化AHRS姿态更新矩阵的解算过程。方法在快速求取变换矩阵的同时,获得平移向量。通过MEMSAHRS摇摆机座仿真试验,验证了方法的可行性。
鉴于AHRS对地磁参考向量的信赖,讨论磁场干扰特性和相应对策,在软件层面上,开展磁航向角误差补偿算法的研究。方法简单易行,适于在实际系统中应用。
提出一种基于强跟踪(ST)性能的高斯二阶EKF(QEKF)自适应滤波算法,将其应用于AHRS/GPS紧耦合导航系统中。建立了伪距—伪距率—航向角组合观测数学模型,引入QEKF方法,补偿系统观测方程线性化的二阶截断误差,逼近组合系统非线性特性。利用ST算法降低对历史状态信息的使用率,快速跟踪系统当前时刻状态突变,以保证滤波器可靠收敛,增强系统鲁棒性能。
借助上述改进的EKF滤波器模型,设计神经网络辅助的组合导航滤波器,体现智能方法和经典控制方法的融合性。根据网络样本数大、及无状态反馈结构设计需要,选取广义径向基函数网络(RBFNN)对滤波器状态方程的预报值进行在线修正,获得的导航参数精度和动态性能均有提高。研究结果表明:在传感器精度有限的情况下,融合滤波方法在低成本AHRS/GPS组合系统中的导航参数估算是可行的。
With the development of MEMS technology, the micro inertial measurement uint (MIMU) is widely used in solid-state low cost strapdown attitude and heading reference system (AHRS). AHRS is a three dimensional measurement package including MIMUs and magnetic sensors, which presents the prominent characteristics of small volume, light weight, high reliability and strong shock-resistantance, accomplishing the real-time attitude and heading control for carriers under dynamic conditions. Moreover, the ideal positioning mode of integrated navigation is achieved by combine AHRS with a miniature GPS.
The extended Kalman filter (EKF) is the effective means when solving data fusion issues of integrated navigation system. Due to the large drifts and poor stability of MEMS sensors, the fusion filter solution is proposed, which fuses the improved EKF methods and intelligent control ones based on artificial netwoks, achieving the optimal estimates of parameters. The dissertation focuses on two parts, namely operation technique on stand-alone AHRS and fusion filter research on tightly-coupled AHRS/GPS.
The strapdown attitude algorithm of micromachined AHRS is researched in-depth based on mastering the operation principle of these types of sensors. Two non-colinear referece vectors, which involve gravity from accelerometers' measurements and geomagnetic field from magnetic fluxgate sensors' measurements, are observed to correct the attitude and heading obtained from gyros' integral. In sequence, the observation update equation of Kalman fiter is constructed in body frame to estimate and compensate gyros' zero bias in real time.
Considering that the attitude updating might be over rapid for land vehicular carriers, shortening attitude updating period is of great essentialness. The dual quaternion (DQ) algebra is introduced, optimizing the calculating process for extracting attitude updating matrix. This leads to calculate the rotation matrix with higher rapid, and provide the estimates of the translation at the same time. The approach is verified to be feasible by simulations under the straight and swing motion base of MEMS AHRS.
Since AHRS relies greatly on the geomagnetic field, the magnetic disturbances and corresponding solutions are discussed. In addition, the software scheme on magnetic heading error compensation is analyzed. It is proved that the method is an easy one, and it adapts to the practical applications.
The adaptive quadratic EKF (QEKF) algorithm based on strong tracking (ST) theory for tightly-coupled AHRS/GPS is proposed, and the parameter model is established by fusing pseudo_range-pseudo_range rate-heading measurements. The quadratic truncation error extracted from linearization is compensated by using QEKF, approaching the nonlinear feature in nature. Through ignoring the prevenient states information based on strong tracking algorithm, the sudden changes of states are rapidly been sensed so as to guarantee the astringency, enhancing the robustness of the whole system.
The neural network aided filter is designed in virtue of the improved EKF model, indicating the fusion feature of intelligent control methods and classical ones. Considering the large training sample and no-feedback net structure, the generalized radial basis function neural network (RBFNN) is selected to modify the predicted states on line. In consequence, the accuracy and kinematic performance are all enhanced as expected. Study has shown that the fusion filtering is feasible for parameter estimates in the field of low cost AHRS/GPS integration, even if the accuracy of the sensors is modest.
扩展卡尔曼滤波(EKF)是处理组合导航系统信息融合最有效的手段,针对MEMS传感器漂移大、稳定性差等特点,开展改进的EKF方法与神经网络智能控制相结合的融合滤波方法,以寻求最佳导航参数估计精度。论文围绕AHRS单机工作算法以及AHRS/GPS紧耦合融合滤波技术两部分开展研究工作。
在掌握各传感器工作机理的基础上,深入研究微机械AHRS捷联姿态算法,利用加速度计和磁通门传感器对重力、地磁两不共线参考向量的观测,修正陀螺积分后的角度漂移,建立载体系下姿态Kalman滤波器观测修正方程,对陀螺零漂进行有效估计和补偿。
从陆用载体姿态变化可能过快这一实际情况出发,阐明缩短载体姿态更新周期的必要性。引入对偶四元数(DQ)理论,以优化AHRS姿态更新矩阵的解算过程。方法在快速求取变换矩阵的同时,获得平移向量。通过MEMSAHRS摇摆机座仿真试验,验证了方法的可行性。
鉴于AHRS对地磁参考向量的信赖,讨论磁场干扰特性和相应对策,在软件层面上,开展磁航向角误差补偿算法的研究。方法简单易行,适于在实际系统中应用。
提出一种基于强跟踪(ST)性能的高斯二阶EKF(QEKF)自适应滤波算法,将其应用于AHRS/GPS紧耦合导航系统中。建立了伪距—伪距率—航向角组合观测数学模型,引入QEKF方法,补偿系统观测方程线性化的二阶截断误差,逼近组合系统非线性特性。利用ST算法降低对历史状态信息的使用率,快速跟踪系统当前时刻状态突变,以保证滤波器可靠收敛,增强系统鲁棒性能。
借助上述改进的EKF滤波器模型,设计神经网络辅助的组合导航滤波器,体现智能方法和经典控制方法的融合性。根据网络样本数大、及无状态反馈结构设计需要,选取广义径向基函数网络(RBFNN)对滤波器状态方程的预报值进行在线修正,获得的导航参数精度和动态性能均有提高。研究结果表明:在传感器精度有限的情况下,融合滤波方法在低成本AHRS/GPS组合系统中的导航参数估算是可行的。
With the development of MEMS technology, the micro inertial measurement uint (MIMU) is widely used in solid-state low cost strapdown attitude and heading reference system (AHRS). AHRS is a three dimensional measurement package including MIMUs and magnetic sensors, which presents the prominent characteristics of small volume, light weight, high reliability and strong shock-resistantance, accomplishing the real-time attitude and heading control for carriers under dynamic conditions. Moreover, the ideal positioning mode of integrated navigation is achieved by combine AHRS with a miniature GPS.
The extended Kalman filter (EKF) is the effective means when solving data fusion issues of integrated navigation system. Due to the large drifts and poor stability of MEMS sensors, the fusion filter solution is proposed, which fuses the improved EKF methods and intelligent control ones based on artificial netwoks, achieving the optimal estimates of parameters. The dissertation focuses on two parts, namely operation technique on stand-alone AHRS and fusion filter research on tightly-coupled AHRS/GPS.
The strapdown attitude algorithm of micromachined AHRS is researched in-depth based on mastering the operation principle of these types of sensors. Two non-colinear referece vectors, which involve gravity from accelerometers' measurements and geomagnetic field from magnetic fluxgate sensors' measurements, are observed to correct the attitude and heading obtained from gyros' integral. In sequence, the observation update equation of Kalman fiter is constructed in body frame to estimate and compensate gyros' zero bias in real time.
Considering that the attitude updating might be over rapid for land vehicular carriers, shortening attitude updating period is of great essentialness. The dual quaternion (DQ) algebra is introduced, optimizing the calculating process for extracting attitude updating matrix. This leads to calculate the rotation matrix with higher rapid, and provide the estimates of the translation at the same time. The approach is verified to be feasible by simulations under the straight and swing motion base of MEMS AHRS.
Since AHRS relies greatly on the geomagnetic field, the magnetic disturbances and corresponding solutions are discussed. In addition, the software scheme on magnetic heading error compensation is analyzed. It is proved that the method is an easy one, and it adapts to the practical applications.
The adaptive quadratic EKF (QEKF) algorithm based on strong tracking (ST) theory for tightly-coupled AHRS/GPS is proposed, and the parameter model is established by fusing pseudo_range-pseudo_range rate-heading measurements. The quadratic truncation error extracted from linearization is compensated by using QEKF, approaching the nonlinear feature in nature. Through ignoring the prevenient states information based on strong tracking algorithm, the sudden changes of states are rapidly been sensed so as to guarantee the astringency, enhancing the robustness of the whole system.
The neural network aided filter is designed in virtue of the improved EKF model, indicating the fusion feature of intelligent control methods and classical ones. Considering the large training sample and no-feedback net structure, the generalized radial basis function neural network (RBFNN) is selected to modify the predicted states on line. In consequence, the accuracy and kinematic performance are all enhanced as expected. Study has shown that the fusion filtering is feasible for parameter estimates in the field of low cost AHRS/GPS integration, even if the accuracy of the sensors is modest.
引文
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