| 1. | Rapid static and kinematic positioning based on gps active network
快速静态定位及动态定位方法 |
| 2. | Application in high precision gps kinematic positioning shows that the approximate form causes markable velocity and acceleration errors
在高精度gps动态定位中的应用证明，常用近似公式对载体速度和加速度有显著的影响。 |
| 3. | The accuracy and reliability of the kinematic positioning are affected by not only the random noises and systematic wrong , but also the observation noises related to time
摘要在动态定位数据处理中，动态定位的精度和可靠性除受观测偶然误差和系统误差的影响外，还受时间相关的观测噪声的影响。 |
| 4. | At first , this paper briefly introduces the background and significance of research on estimation theory of colored noises . the influence function ( if ) ot the colored noises on the kinematic positioning is derived and analysed
首先，简要地介绍了有色噪声估计理论的研究背景及意义，分析了有色噪声的影响函数、变化规律和两种传统处理方法的优缺点。 |
| 5. | In view of this problem of kinematic positioning , this thesis discusses kalman filtering when model biases exist in practical applications , studies model bias detecting and correcting of kalman filtering with kinematic positioning , and provides a departing estimation algorithm of model biases
探讨在实际应用中存在模型误差时的卡尔曼滤波，研究动态定位时卡尔曼滤波的模型检测与校正，给出一种偏差分离估计方法。 |
| 6. | The results show that saastamoinen / niell model can remove the most of the tropospheric delay and then significantly improve the kinematic positioning solutions ; the ionosphere which is modeled on random walk process can also improve the kinematic positioning solutions ; however , the troposphere which is modeled on random walk process will bias the kinematic positioning solutions
结果表明，对流层模型改正可以大大改善定位结果的精度，不过仍存在未模型化的对流层延迟误差。将电离层延迟作为随机过程来处理，可以提高定位精度；而将对流层延迟作为随机过程来处理，则会影响定位精度。 |
| 7. | Kalman filtering is widely used for data processing in kinematic gps positioning , while the practical application of kalman filtering requires the dynamic model ( functional model ) and the stochastic model to be reliable and accurate , yet it is difficult to maintain regular motion of the object in actual kinematic positioning , thus model biases are usually generated
摘要动态定位的数据处理中广泛应用卡尔曼滤波，而卡尔曼滤波的应用要求动态模型（函数模型）和随机模型可靠和切合实际，但实际测量定位中难以保证观测对象的规则运动，因而容易出现模型误差。 |
