排序方式： 共有4条查询结果，搜索用时 0 毫秒
1
1.
卞逸驰;王生亮;肖恭伟;岳利民;王宁波;杨一博 《全球定位系统》2025,50(3):31-37
随着GNSS的不断发展,基于精密单点定位(precise point positioning,PPP)技术的高精度时间传递研究已成为时频领域的关键技术之一. 本文以北斗三号系统解算接收机钟差为研究对象,分析了无电离层(ionospheric-free,IF)组合和非组合(uncombined,UC)PPP模型对时间传递的影响. 首先推导了两种PPP数学模型差异,选取2024年年积日第79～81天的BRUX和USN7两个外接氢原子钟测站观测数据进行分析. 结果表明：两种PPP模型解算的接收机钟差差值在0.15 ns内波动,修正的Allan方差数值接近;对BRUX-USN7时间链路对比分析,3天内时间传递结果均在0.8 ns内波动,两种模型解算钟差修正的Allan方差数值近似. 综上,两种PPP模型虽然钟差数值解算的理论模型不同,但在钟差解算和时间传递方面无明显差异,非组合模型可以用来进行时间传递且在保留电离层信息方面更具有优势. 相似文献
2.
In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3 km resolution (AMSEAS), and two versions of the Hybrid Coordinates Ocean Model (HYCOM) set at 4 km were running daily and delivering 72-h range forecasts. They all assimilated remote sensing and local profile data but they were not assimilating the drifter’s observations. This work presents a non-intrusive methodology named Multi-Model Ensemble Kalman Filter that allows assimilating the local drifter data into such a set of models, to produce improved ocean currents forecasts. The filter is to be used when several modeling systems or ensembles are available and/or observations are not entirely handled by the operational data assimilation process. It allows using generic in situ measurements over short time windows to improve the predictability of local ocean dynamics and associated high-resolution parameters of interest for which a forward model exists (e.g. oil spill plumes). Results can be used for operational applications or to derive enhanced background fields for other data assimilation systems, thus providing an expedite method to non-intrusively assimilate local observations of variables with complex operators. Results for the GLAD experiment show the method can improve water velocity predictions along the observed drifter trajectories, hence enhancing the skills of the models to predict individual trajectories. 相似文献
3.
针对超宽带(ultra wide band,UWB)和微机电系统(micro electro mechanical system,MEMS)惯性测量单元(inertial measurement unit,IMU)紧组合滤波系统中,UWB测距信号存在较大的非视距(non line of sight,NLOS)误差及滤波系统异常,易造成噪声参数与真实噪声的概率分布严重不符,导致整个滤波系统发散甚至崩溃,严重影响紧组合系统性能的问题. 提出UWB/MEMS IMU紧组合自适应扩展卡尔曼滤波算法(adaptive restimation extended Kalman filtering,AREKF),充分发挥惯性导航系统(inertial navigation system,INS)短时高精度特性,对UWB观测值进行异常探测,利用新息序列对观测噪声和预测状态误差协方差矩阵进行自适应调节,降低异常观测或NLOS误差的影响,有效防止滤波器过度收敛、发散、崩溃现象. 动态行人实验结果表明,UWB/MEMS IMU紧组合AREKF方法N和E方向均方根(root mean square,RMS)优于0.3 m,U方向RMS优于1.3 m,较UWB加权最小二乘法(weighted least squares,WLS)、UWB扩展卡尔曼滤波(extended Kalman filter,EKF)、UWB/MEMS IMU松组合EKF 和UWB/MEMS IMU紧组合EKF有效提升了定位系统的精度、稳定性和可靠性.
相似文献
1
