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1.
A new approach for airborne vector gravimetry using GPS/INS 总被引:2,自引:2,他引:2
A new method for airborne vector gravimetry using GPS/INS has been developed and the results are presented. The new algorithm
uses kinematic accelerations as updates instead of positions or velocities, and all calculations are performed in the inertial
frame. Therefore, it is conceptually simpler, easier, more straightforward and computationally less expensive compared to
the traditional approach in which the complex navigation equations should be integrated. Moreover, it is a unified approach
for determining all three vector components, and no stochastic gravity modeling is required. This approach is based on analyzing
the residuals from the Kalman filter of sensor errors, and further processing with wavenumber coefficient filterings is applied
in case closely parallel tracks of data are available. An application to actual test-flight data is performed to test the
validity of the new algorithm. The results yield an accuracy in the down component of about 3–4 mGal. Also, comparable results
are obtained for the horizontal components with accuracies of about 6 mGal. The gravity modeling issue is discussed and alternative
methods are presented, none of which improves on the original approach.
Received: 18 April 2000 / Accepted: 14 August 2000 相似文献
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简要介绍了GPS/INS松组合导航系统状态方程和观测方程。针对标准Kalman滤波算法存在的状态方程截断误差、噪声统计特性的不确定性以及状态扰动异常的影响,给出了一种应用于GPS/INS组合导航系统的迭代滤波算法。该算法采用迭代策略,不断利用观测信息实时修正状态预报值。实测数据计算结果表明,通过对状态预报值的实时修正,该算法能够很好地抑制状态预报信息的不确定性和扰动异常等对导航解的影响。其滤波解精度明显优于标准Kalman滤波。 相似文献
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为验证导航模型在GPS信号更新频率较低的情况下的导航能力,给出了GPS/INS组合导航滤波模型。载体运动过程中,分别使信号中断5s、10s、15s。通过实验得出,GPS信号中断时间过长(10s以上),GPS信号恢复后,Kalman滤波器会产生发散现象。引入支持向量机,提出利用SVM内插GPS信号提高信息更新频率消除组合导航滤波器的发散。结果表明,GPS信号中断时间过长导致组合导航系统滤波发散的情况下,通过SVM内插GPS数据提高GPS更新频率,可以有效地抑制滤波发散,提高导航的准确性。 相似文献
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基于抗差EKF的GNSS/INS紧组合算法研究 总被引:2,自引:0,他引:2
提出了GNSS/INS紧组合导航的抗差EKF算法,采用21状态GNSS/INS紧组合状态方程,根据多余观测分量及预测残差统计构造抗差等价增益矩阵,建立抗差EKF算法,通过迭代给出GNSS/INS组合导航的抗差解,并开发GNSS/INS紧组合导航模拟平台,通过对观测值加入单粗差、多粗差及缓慢增长三类误差,测试本文算法对不同粗差的抑制能力。分析表明,抗差EKF可以将三类粗差抑制在相应观测值的残差中,达到削弱其对状态参数估计的影响。本文算例证明,抗差EKF算法可将导航解的误差精度从dm级提高为cm级甚至mm级,导航精度及可靠性得到明显提高。 相似文献
6.
GPS/INS组合导航系统抗差滤波器设计 总被引:5,自引:0,他引:5
常规Kalman滤波器已经广泛用于GPS/INS组合导航系统,其中假设系统动态模型和噪声统计特性是精确已知的。事实上,这种假设是不符合实际情况的。在组合导航系统中,惯性测量器件的质量不稳定,GPS测量误差受外界环境的影响,因而对组合导航系统进行抗差设计是十分必要的。本文利用对策论设计了能使不确定噪声下性能最好的极小极大抗差滤波器,并将其应用到GPS/INS组合导航系统中。考虑一个IO状态的GPS/ 相似文献
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GPS/INS组合导航非线性系统最优估计算法中,基于统计信息和假设检验理论的多渐消因子自适应滤波算法的应用前提条件是残差向量为高斯白噪声。本文针对观测异常会影响残差向量的数字特性分布,提出了一种神经网络辅助的多重渐消因子自适应SVD-UKF算法。该算法采用神经网络算法削弱观测异常对残差序列高斯白噪声分布特性的影响,利用奇异值分解抑制UKF中先验协方差矩阵负定性变化,同时构造多重渐消因子对预测状态协方差阵进行调整,使得不同的滤波通道具有不同的调节能力,高效地应用于多变量复杂系统。最后利用车载实测数据进行了验证。结果表明,神经网络算法极大削弱了观测粗差对残差序列高斯白噪声分布特性的影响,拓展了多重渐消因子的应用范围,使其能在观测值含有粗差的条件下自适应调节不同滤波通道,消除滤波状态中的异常,提高组合导航解的精度和可靠性。 相似文献
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Comparing the Kalman filter with a Monte Carlo-based artificial neural network in the INS/GPS vector gravimetric system 总被引:3,自引:1,他引:3
X. Li 《Journal of Geodesy》2009,83(9):797-804
Rigorous physical and mathematical analysis has been intensively developed to obtain the gravity disturbance vector from the
inertial navigation system and the global positioning system. However, the combination of the observation noise and the systematic
INS errors make it very challenging to accurately and efficiently describe the dynamics of the system with rigorous equations.
Thus, the accuracy of the gravity disturbance estimates, especially in the horizontal components, is limited by the insufficient
error models. To overcome the difficulty of directly modeling the systematic errors with exact mathematical equations, a Monte
Carlo based artificial neural network is successfully applied in the moving base gravimetric system. The computation results
show significant improvement in the precision of all components of the gravity disturbance estimates. 相似文献
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常规GPS/INS紧组合抗差自适应滤波只适用于卫星数≥4的情况,且预测残差构造自适应因子要求观测值可靠。针对该局限性,对常规抗差自适应滤波算法做出两点改进:1)采用两步滤波,用第1步常规EKF滤波残差构造第二步抗差算法的粗差判别量;2)在第2步滤波用预测残差构造自适应因子时,剔除异常观测值对应的预测残差和预测残差协方差,以削弱观测异常对自适应因子的不良影响。实验结果表明,常规抗差算法在卫星数4时不适用。常规自适应滤波算法在观测值存在异常的情况下无法正确修正模型异常。改进后的抗差自适应滤波算法在组合系统观测卫星数4且观测值存在异常的情况下,仍能正确修正观测粗差和动力学模型异常,能够达到良好的导航精度。 相似文献
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在GPS/INS组合导航中,传统UKF(Unscented Kalman Filter)计算量大,无法满足实时性要求。而且当动力学模型受到异常扰动误差影响时,其精度与稳定性易受到影响。针对以上问题,利用最小偏度单形采样策略降低UKF计算量以提高精度;通过自适应调整过程噪声以降低动态异常扰动误差对UKF精度与稳定性的影响。由此提出了一种改进UKF算法,用于GPS/INS组合导航。仿真实验结果表明,改进UKF算法用于GPS/INS组合导航的精度要优于UKF算法。 相似文献
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在提出单历元解算GPS模糊度技术的基础上,使用INS间接法误差模型,提出了一种GPS双差载波相位多普勒/INS全组合Kalman滤波的新方法,高精度直接解算机载TLS中的外方位元素。仿真数据试验表明,其姿态精度高于10",位置精度高于10cm。 相似文献
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This paper will describe an enhancement to the GPS double difference carrier phase measurements on a single airborne platform
by smoothing them with inertial measurements while preserving the dynamic bandwidth. This enhancement will reduce the impact
of carrier phase multipath and carrier phase noise on baseline determination between multiple antennas on an aircraft when
in flight. This type of measurement system has numerous applications where platform pointing and relative body motion must
be determined at the mm-level for applications such as sensor stabilization, Synthetic Aperture Radar, long range RADAR (i.e.
angle-of-arrival measurements). Lower noise levels (mm-level and below) enable more performance to the stabilized system such
as increased aperture for longer range, operation at higher frequencies, and more image resolution. The focus of this paper
will be on a technique to provide this enhanced performance for these various applications using the available navigation
systems. Additionally, this type of smoothing can effectively remove the additional noise induced by carrier phase measurement
differencing. The noise level of a double or triple difference can be reduced below that of the raw measurement. A complimentary
synthesized double difference quantity with ultra-low-noise characteristics will be used to smooth the GPS carrier phase double
difference measurements without losing dynamic bandwidth since it follows the airborne dynamics. Flight test data will be
presented to demonstrate the performance improvement in the midst of aircraft dynamics. Results will show that the noise reduction
follows the theoretical prediction. 相似文献
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Heather J. Richardson David J. Hill Dan R. Denesiuk Lauchlan H. Fraser 《地理信息系统科学与遥感》2017,54(4):573-591
We used geographic datasets and field measurements to examine the mechanisms that affect soil carbon (SC) storage for 65 grazed and non-grazed pastures in southern interior grasslands of British Columbia, Canada. Stepwise linear regression (SR) modeling was compared with random forest (RF) modeling. Models produced with SR performed better than those produced using RF models (r2 = 0.56–0.77 AIC = 0.16–0.30 for SR models; r2 = 0.38–0.53 and AIC = 0.18–0.30 for RF models). The factors most significant when predicting SC were elevation, precipitation, and the normalized difference vegetation index (NDVI). NDVI was evaluated at two scales using: (1) the MOD 13Q1 (250 m/16-day resolution) NDVI data product from the moderate resolution imaging spectro-radiometer (MODIS) (NDVIMODIS), and (2) a handheld multispectral radiometer (MSR, 1 m resolution) (NDVIMSR) in order to understand the potential for increasing model accuracy by increasing the spatial resolution of the gridded geographic datasets. When NDVIMSR data were used to predict SC, the percentage of the variance explained by the model was greater than for models that relied on NDVIMODIS data (r2 = 0.68 for SC for non-grazed systems, modeled with SR based on NDVIMODIS data; r2 = 0.77 for SC for non-grazed systems, modeled with SR based on NDVIMSR data). The outcomes of this study provide the groundwork for effective monitoring of SC using geographic datasets to enable a carbon offset program for the ranching industry. 相似文献