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联邦滤波在组合导航的应用中,具有容错性好、滤波精度高、计算量小以及实时性好的特点,但在无法得到准确的系统模型时,使用联邦滤波会出现滤波精度低甚至发散的情况。针对车载组合导航信息融合的高精度、高可靠性等要求,提出了一种组合导航的自适应联邦滤波算法。其主要思想是以判别观测数据中的野值存在与否为算法切换条件,存在野值时采用改进的增益矩阵滤波处理方法,不存在野值时则采用模糊自适应联邦滤波方法。将此方法用于SINS/GPS车载组合导航系统中,实验表明,采用的这种自适应滤波方法,能够有效抑制滤波发散,其滤波精度和收敛速度要优于常规联邦滤波,是一种有效的车载组合导航算法。 相似文献
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针对采用低成本惯性元件的MEMS-IMU/GPS组合导航系统在GPS信号长时间失效后系统趋于发散的问题,在研究捷联航姿解算算法、加速计测量姿态角算法和磁传感器解算航向角算法的基础上,提出将航姿辅助修正算法引入组合导航系统中,一旦系统检测到无效信号出现,则能自动切换到辅助修正状态,使导航结果被抑制在0.5°左右;通过半实物仿真实验,证明了该方法能够有效解决GPS信号长时间失效后系统发散问题,满足了在低成本MEMS惯性元件基础上的高精度导航要求。 相似文献
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MEMS IMU/GPS组合导航系统的应用环境愈来愈复杂,对其精度的要求也愈来愈高,只使用普通卡尔曼滤波不能满足精度和稳定性要求。针对此问题,将Sage-husa自适应卡尔曼滤波算法和非完整约束应用到前向导航滤波算法和后向导航滤波算法中,并将前向滤波和后向滤波结果加权组合,提出了一种非完整约束下加权组合滤波算法,用于事后IMU/GPS联合解算中,用来提高组合导航的精度。并利用实验室设备进行车载实验,通过实测车载数据解算结果来验证该方法的可行性。实验结果表明非完整约束下加权组合滤波后的经纬度误差小于1.4 m,航向角误差小于1.0°,满足MEMS IMU/GPS车载组合导航系统的精度要求。 相似文献
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不完备条件下GPS定位算法的研究 总被引:3,自引:0,他引:3
利用全球定位系统(GPS)能在观测到3颗GPS卫星时实现2D定位,实现3D定位则需要至少观测到4颗卫星。在卫星观测数目为2或1的不完备条件下,仅利用GPS已无法完成正常定位。文章提出了结合数字地图的不完备条件下GPS定位算法,实验证明,应用该算法在仅能观测到2颗GPS卫星时能完成满足一定精度要求的定位,在仅能观测到1颗GPS卫星时能给出有价值的信息,并且能够简化地图匹配的工作,从而可以作为车载定位系统的有力补充。 相似文献
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Kamal Saadeddin Mamoun F. Abdel-Hafez Mohammad A. Jaradat Mohammad Amin Jarrah 《Journal of Intelligent and Robotic Systems》2014,73(1-4):325-348
Due to the inherent highly nonlinear vehicle state error dynamics obtained from low-cost inertial navigation system (INS) and Global Positioning System (GPS) along with the unknown statistical properties of these sensors, the optimality/accuracy of the classical Kalman filter for sensor fusion is not guaranteed. Therefore, in this paper, low-cost INS/GPS measurement integration is optimized based on different artificial intelligence (AI) techniques: Neural Networks (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) architectures. The proposed approaches are aimed at achieving high-accuracy vehicle state estimates. The architectures utilize overlapping windows for delayed input signals. Both the NN approaches and the ANFIS approaches are used once with overlapping position windows as the input and once with overlapping position and velocity windows as the input. Experimental tests are conducted to evaluate the performance of the proposed AI approaches. The achieved accuracy is presented and discussed. The study finds that using ANFIS, with both position and velocity as input, provides the best estimates of position and velocity in the navigation system. Therefore, the dynamic input delayed ANFIS approach is further analyzed at the end of the paper. The effect of the input window size on the accuracy of state estimation is also discussed. 相似文献
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卫星导航系统实现导航定位功能要求可见星数大于等于4颗,这一限制影响了卫星导航系统在信号遮挡严重环境下的应用,如城市峡谷、大型桥梁等。针对这一问题,提出了将卫星导航系统与低成本MEMS惯性器件(IMU)进行信息融合的方案并用硬件实现该系统。将GPS与MEMS惯性器件以紧组合的工作模式融合,采用卫星原始星历信息作为观测量建立观测模型并以卡尔曼滤波实现信息融合,该系统在少于4颗可见星时仍能实现导航。为了验证这一方案,进行了仿真试验和跑车实验,结果表明该系统在可见星数小于4颗的情况下仍能保持较好的导航精度。 相似文献
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为增强机载捷联惯导系统(SINS)在自标定过程中的可观测性,提升陀螺仪漂移和加速度计零偏估计的速度和精度,引入星敏感器姿态信息和GPS速度信息,辅助完成捷联惯导系统的空中标定。同时,考虑在实际空中飞行条件下,受气流、电磁干扰等影响,姿态和速度的量测噪声呈非高斯分布且噪声统计特性不精确,导致经典卡尔曼滤波性能降低。为有效利用量测信号中的高阶矩信息,在卡尔曼滤波中采用最大熵准则代替最小均方误差准则,对星敏感器辅助下的机载捷联惯导系统的误差进行标定。仿真结果表明,经最大熵卡尔曼滤波后,惯性器件误差的标定精度明显提升;在采用星敏感器后,对陀螺仪漂移的标定速度和精度都得到了提升。 相似文献
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为解决GPS信号失锁条件下,GPS/INS(inertial navigation system)组合导航系统解算精度降低甚至发散的问题,提出采用多层感知机神经网络(multilayer perceptron neural networks,MLPNN)来辅助组合导航系统.在GPS信号有效时对神经网络进行训练,在GPS... 相似文献
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总结了常用的自适应滤波的方法,并提出了一种基于模糊逻辑的自适应卡尔曼滤波技术,用模糊逻辑自适应推理器来“在线”修正卡尔曼滤波系统噪声协方差Q和测量噪声协方差R,从而使滤波器不断执行最优估计。仿真结果表明该方法可以提高GPS/INS组合导航系统的精度和可靠性。 相似文献
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Kalman filter (KF) is the most commonly used estimation technique for integrating signals from short-term high performance systems, like inertial navigation systems (INSs), with reference systems exhibiting long-term stability, like the global positioning system (GPS). However, KF only works well under appropriately predefined linear dynamic error models and input data that fit this model. The latter condition is rather difficult to be fulfilled by a low-cost inertial measurement unit (IMU) utilizing microelectromechanical system (MEMS) sensors due to the significance of their long- and short-term errors that are mixed with the motion dynamics. As a result, if the reference GPS signals are absent or the Kalman filter is working for a long time in prediction mode, the corresponding state estimate will quickly drift with time causing a dramatic degradation in the overall accuracy of the integrated system. An auxiliary fuzzy-based model for predicting the KF positioning error states during GPS signal outages is presented in this paper. The initial parameters of this model is developed through an offline fuzzy orthogonal-least-squares (OLS) training while the adaptive neuro-fuzzy inference system (ANFIS) is implemented for online adaptation of these initial parameters. Performance of the proposed model has been experimentally verified using low-cost inertial data collected in a land vehicle navigation test and by simulating a number of GPS signal outages. The test results indicate that the proposed fuzzy-based model can efficiently provide corrections to the standalone IMU predicted navigation states particularly position. 相似文献
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针对MEMS-IMU/GPS组合导航系统数据融合时坐标系不统一的问题,提出了一种由GPS坐标系(WGS-84坐标系)到惯性坐标系(当地水平游移坐标系)的转换方法。该方法在由GPS坐标系转换到西北天惯性坐标系的基础上,计算与西向的游移偏航角,最终完成坐标系的统一。经实际的跑车实验验证,由GPS信息转换到惯性坐标系下的偏航角与惯性偏航角偏差小于1°,精确的实现两坐标系的统一,在实际的工程应用中具有一定的参考价值。 相似文献