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1.
Conventional Kalman filter (KF) relies heavily on a priori knowledge of the potentially unstable process and measurement noise statistics. Insufficiently known a priori filter statistics will reduce the precision of the estimated states or introduce biases to the estimates. We propose an adaptive KF based on the autoregressive (AR) predictive model for vehicle navigation. First, the AR model is incorporated into the KF for state estimation. The closed-form solution of the AR model coefficients is obtained by solving a convex quadratic programming problem, which is according to the criterion of minimizing the mean-square error, and subject to the polynomial constraint of vehicle motion. Then, an innovation-based adaptive approach is improved based on the KF with the AR predictive model. In the proposed adaptive algorithm, the process noise covariance is computed using the real-time information of the innovation sequence. Simulation results demonstrate that the KF with the AR model has a higher estimated precision than the KF with the traditional discrete-time differential model under the condition of the same parameter setting. Field tests show that the positioning accuracy of the proposed adaptive algorithm is superior to the conventional adaptive KF. 相似文献
2.
运用现代时间序列分析方法对ＳＡ定位误差模型进行建模研究，并由此提出抗ＳＡ影响的卡尔曼德彼模型。 相似文献
3.
Dead reckoning techniques such as inertial navigation and odometry are integrated with GPS to avoid interruption of navigation
solutions due to lack of visible satellites. A common method to achieve a low-cost navigation solution for land vehicles is
to use a MEMS-based inertial measurement unit (IMU) for integration with GPS. This integration is traditionally accomplished
by means of a Kalman filter (KF). Due to the significant inherent errors of MEMS inertial sensors and their time-varying changes,
which are difficult to model, severe position error growth happens during GPS outages. The positional accuracy provided by
the KF is limited by its linearized models. A Particle filter (PF), being a nonlinear technique, can accommodate for arbitrary
inertial sensor characteristics and motion dynamics. An enhanced version of the PF, called Mixture PF, is employed in this
paper. It samples from both the prior importance density and the observation likelihood, leading to an improved performance.
Furthermore, in order to enhance the performance of MEMS-based IMU/GPS integration during GPS outages, the use of pitch and
roll calculated from the longitudinal and transversal accelerometers together with the odometer data as a measurement update
is proposed in this paper. These updates aid the IMU and limit the positional error growth caused by two horizontal gyroscopes,
which are a major source of error during GPS outages. The performance of the proposed method is examined on road trajectories,
and results are compared to the three different KF-based solutions. The proposed Mixture PF with velocity, pitch, and roll
updates outperformed all the other solutions and exhibited an average improvement of approximately 64% over KF with the same
updates, about 85% over KF with velocity updates only, and around 95% over KF without any updates during GPS outages. 相似文献
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设计一种组合GPS／速率陀螺定姿系统。系统以方向余弦矩阵表示姿态,建立GPS／速率陀螺组合状态模型和观测模型。结合kalman滤波算法,提出一种状态矩阵卡尔曼滤波（StateMatrixKalmanKilter,SMKF）姿态估计算法,并采用拉格朗日算法对姿态矩阵进行正交化约束。与传统的基于四元数的扩展卡尔曼滤波（EKF）算法相比,基于方向余弦矩阵的姿态系统状态方程与测量方程均为线性方程,无需线性化处理,对初始姿态误差更具有较好的鲁棒性。数值仿真表明,该方法具有精度高和稳定性强等优点。 相似文献
6.
针对实际环境中量测噪声易被野值污染而呈现非高斯分布,进而导致传统卡尔曼滤波(KF)算法性能降低的问题,提出了最大熵卡尔曼滤波(MCKF)算法. 该算法基于最大熵准则(MCC)和M估计的思想推导得到. 与KF相比,所提算法能够给异常量测值分配较小的权重以减轻其对于状态估计的影响,与基于Huber函数的卡尔曼滤波(HKF)算法相比,其能够更有效地利用量测信息,因此所提算法相比于KF和HKF而言更加鲁棒. 在全球卫星导航系统(GNSS)与惯性导航系统(INS)的紧组合模式下进行车载实测实验,由于GNSS的伪距与伪距率等原始量测信息质量不佳,因此KF和HKF的性能均受到影响,而所提MCKF算法能够有效地抑制异常量测值的影响,能够更快地收敛且得到更高的估计精度. 相似文献
7.
Digital mobile mapping, the method that integrates digital imaging with direct geo-referencing, has developed rapidly over
the past 15 years. The Kalman filter (KF) is considered an optimal estimation tool for real-time INS/GPS integrated kinematic
positioning and orientation determination. However, the accuracy requirements of general mobile mapping applications cannot
be easily achieved even when using the KF scheme. Therefore, this study proposes an intelligent scheme combining ANN and RTS
backward smoother to overcome the limitations of KF and to enhance the overall accuracy of attitude determination for tactical
grade and MEMS INS/GPS integrated systems.
相似文献
| Yun-Wen Huang (Corresponding author)Email: |
8.
Performance improvement of integrated Inertial Measurement Units (IMU) utilizing micro-electro-mechanical-sensors (MEMS) and
GPS is described in this paper. An offline pre-defined Fuzzy model is employed to improve the system performance. The Fuzzy
model is used to predict the position and velocity errors, which are the inputs to a Kalman Filter (KF) during GPS signal
outages. The proposed model has been verified on real MEMS inertial data collected in a land vehicle test. A number of 30-s
GPS outages were simulated during the data processing at different times and under different vehicle dynamics. Performance
of the suggested Fuzzy model was compared to that of the traditional KF particularly during the simulated GPS outages. The
test results indicate that the proposed Fuzzy model can efficiently compensate for GPS updates during short outages. 相似文献
9.
GPS Solutions - The frequency-assisted phase tracking (FAPT) is investigated extensively in comparison with the phase tracking (PT) in a Kalman filter (KF) frame to explore the role the augmented... 相似文献
10.
GPS Solutions - We investigate and quantitatively analyze the similarities and differences between traditional phase-locked loops (PLLs) and Kalman filter (KF)-based PLLs. We focus on three... 相似文献
11.
M. R. Mosavi 《GPS Solutions》2006,10(2):97-107
Position information obtained from standard global positioning system (GPS) receivers has time variant errors. For effective use of GPS information in a navigation system, it is essential to model these errors. A new approach is presented for improving positioning accuracy using neural network (NN), fuzzy neural network (FNN), and Kalman filter (KF). These methods predict the position components’ errors that are used as differential GPS (DGPS) corrections in real-time positioning. Method validity is verified with experimental data from an actual data collection, before and after selective availability (SA) error. The result is a highly effective estimation technique for accurate positioning, so that positioning accuracy is drastically improved to less than 0.40 m, independent of SA error. The experimental test results with real data emphasize that the total performance of NN is better than FNN and KF considering the trade-off between accuracy and speed for DGPS corrections prediction. 相似文献
12.
针对超宽带导航定位中量测信息异常误差和非线性滤波问题,该文提出了一种基于自适应抗差卡尔曼滤波-无迹卡尔曼滤波(KF-UKF)的超宽带导航定位算法。该算法首先利用卡尔曼滤波计算预测状态向量及其协方差矩阵,利用无迹卡尔曼滤波进行量测更新;然后利用先验阈值和预测残差构建量测噪声的抗差协方差矩阵,以减少量测信息异常误差的影响,同时利用自适应因子对算法进行调节和修正。结果表明,该算法能有效地抑制并消除超宽带测距中量测信息异常误差的影响,能有效地处理状态模型误差的影响,提高超宽带导航定位的精度和稳定性,同时拥有比无迹卡尔曼滤波算法更高的计算效率。 相似文献
13.
Vijay K. Agarwal R. Ramani R. M. Patel Meoss Project 《Journal of the Indian Society of Remote Sensing》1987,15(1):55-65
During 1986–1996, several satellite missions on Stereoscopy are planned. This paper sumarises an attempt in evaluating these stereoscopic images for cartographic purposes. For the purpose, we have taken into account a proposed Indian experiment on satellite stereoscopy, which will be based on push-broom type scanning and a ground resolution of 70m×70m. *** DIRECT SUPPORT *** A04KF003 00004 相似文献
14.
将UKF(Unscented Kalman Filter)方法用于惯性/重力组合导航系统.UKF方法设计了少量的呈高斯分布的σ点,在每个更新过程中,σ点随着非线性状态方程和测量方程传播,从而获得滤波值及较高的计算精度,而且避免了对非线性方程的线性化过程.仿真结果表明:UKF方法比传统卡尔曼滤波及其改进的滤波模型都有更高的估计精度,并能有效的克服非线性严重时出现的滤波发散问题. 相似文献
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16.
An enhanced MEMS-INS/GNSS integrated system with fault detection and exclusion capability for land vehicle navigation in urban areas 总被引：2，自引：2，他引：0
We describe an enhanced quality control algorithm for the MEMS-INS/GNSS integrated navigation system. It aims to maintain the system’s reliability and availability during global navigation satellite system (GNSS) partial and complete data loss and disturbance, and hence to improve the system’s performance in urban environments with signal obstructions, tunnels, bridges, and signal reflections. To reduce the inertial navigation system (INS) error during GNSS outages, the stochastic model of the integration Kalman filter (KF) is informed by Allan variance analysis and the application of a non-holonomic constraint. A KF with a fault detection and exclusion capability is applied in the loosely and tightly coupled integration modes to reduce the adverse influence of abnormal GNSS data. In order to evaluate the performance of the proposed navigation system, road tests have been conducted in an urban area and the system’s reliability and integrity is discussed. The results demonstrate the effectiveness of different algorithms for reducing the growth of INS error. 相似文献
17.
郭杭 《武汉大学学报(信息科学版)》1999,(2)
标准的卡尔曼滤波可以扩展到非线性模型,即将泰勒公式应用于状态方程和观测方程,得到扩展卡尔曼滤波公式。首先推导了计算公式,研究了迭代计算方法,并将其用于ＧＰＳ数据的实时处理。 相似文献
18.
利用线形流形的射影方法推导出新息序列统计特性,构造新息AKF,基于新息序列不断地修正状态噪声和量测噪声,实时地反映模型当前真实的统计特性。通过隔河岩大坝实测数据处理,表明该方法能很好地提高随机模型不准确和变形突变影响下的变形估计与预报精度。 相似文献
19.
冯磊 《测绘与空间地理信息》2020,(2):208-212,215
在介绍国内外高铁沉降数据处理方面的研究现状的基础上,依次阐述了包括回归分析法、人工神经网络、灰色系统理论和时间序列分析法在内的经典沉降数据处理方法,着重讲解了标准卡尔曼滤波理论及其相关公式,介绍了两种自适应卡尔曼滤波理论:方差分量估计AKF、方差补偿AKF。本文针对某具体工程实例,分别基于MATLAB平台编写了一套标准卡尔曼滤波程序和一套自适应卡尔曼滤波程序,并运用程序对其作了相关分析。通过对比分析,证明了自适应KF的优越性,并得到一套在处理实际问题时具有一定可行性的模型。 相似文献
20.
传统精密单点定位(PPP)具有高精度、操作方便等诸多优点,其通常利用Kalman滤波进行未知参数的解算,但是定位性能依赖于准确的动态模型和滤波初值,如果动态模型不准确或者滤波初值设定的不正确会导致滤波性能下降甚至发散.针对该问题,提出了一种附加先验的基线约束信息的双站协同PPP定位方法,算法利用双站所成基线的方向信息和长度信息对Kalman滤波过程中双站位置的估计值进行修正,减小了浮点解的误差协方差矩阵,提高了浮点解的精度.利用实测的全球定位系统(GPS)数据进行PPP实验,实际结果表明,与传统PPP参数估计模型相比,本方法有效改善了定位的精度,缩短了收敛时间. 相似文献