A new nonlinear algorithm is proposed for strapdown inertial navigation system （SINS）/celestial navigation system （CNS）/global positioning system （GPS） integrated navigation systems. The algorithm employs a nonl...A new nonlinear algorithm is proposed for strapdown inertial navigation system （SINS）/celestial navigation system （CNS）/global positioning system （GPS） integrated navigation systems. The algorithm employs a nonlinear system error model which can be modified by unscented Kalman filter （UKF） to give predictions of local filters. And these predictions can be fused by the federated Kalman filter. In the system error model, the rotation vector is introduced to denote vehicle＇s attitude and has less variables than the quaternion. Also, the UKF method is simplified to estimate the system error model, which can both lead to less calculation and reduce algorithm implement time. In the information fusion section, a modified federated Kalman filter is proposed to solve the singular covariance problem. Specifically, the new algorithm is applied to maneuvering vehicles, and simulation results show that this algorithm is more accurate than the linear integrated navigation algorithm.展开更多
This work describes the barometric altimetry as virtual constellation applied to the Chinese Area Positioning System (CAPS), which uses the transponders of communication satellites to transfer navigation messages to u...This work describes the barometric altimetry as virtual constellation applied to the Chinese Area Positioning System (CAPS), which uses the transponders of communication satellites to transfer navigation messages to users. Barometric altimetry depends on the relationship of air pressure varying with altitude in the Earth’s atmosphere. Once the air pressure at a location is measured the site altitude can be found. This method is able to enhance and improve the availability of three-dimensional positioning. The difficulty is that the relation between barometric pressure and altitude is variable in different areas and under various weather conditions. Hence, in order to obtain higher accuracy, we need to acquire the real-time air pressure corresponding to an altimetric region’s reference height. On the other hand, the altimetry method will be applied to satellite navigation system, but the greatest difficulty lies in how to get the real-time air pressure value at the reference height in the broad areas overlaid by satellite navigation. We propose an innovational method to solve this problem. It is to collect the real-time air pressures and temperatures of the 1860 known-altitude weather observatories over China and around via satellite communication and to carry out time extrapolation forecast uniformly. To reduce data quantity, we first partition the data and encode them and then broadcast these information via navigation message to CAPS users’ receivers. Upon the interpolations being done in receivers, the reference air pressure and temperature at the receiver’s nearby place is derived. Lastly, combing with the receiver-observed real air pressure and temperature, the site’s altitude can be determined. The work is presented in the following aspects: the calculation principle, formulae, data collection, encoding, prediction, interpolation method, navigation message transmission together with errors causes and analyses. The advantages and shortcomings of the technique are discussed at the end.展开更多
基金supported by the National Natural Science Foundation of China (60535010)
文摘A new nonlinear algorithm is proposed for strapdown inertial navigation system （SINS）/celestial navigation system （CNS）/global positioning system （GPS） integrated navigation systems. The algorithm employs a nonlinear system error model which can be modified by unscented Kalman filter （UKF） to give predictions of local filters. And these predictions can be fused by the federated Kalman filter. In the system error model, the rotation vector is introduced to denote vehicle＇s attitude and has less variables than the quaternion. Also, the UKF method is simplified to estimate the system error model, which can both lead to less calculation and reduce algorithm implement time. In the information fusion section, a modified federated Kalman filter is proposed to solve the singular covariance problem. Specifically, the new algorithm is applied to maneuvering vehicles, and simulation results show that this algorithm is more accurate than the linear integrated navigation algorithm.
基金Supported by the National Basic Research Program of China (Grant No. 2007CB815500)the National High Technology Research and Development Program (Grant No. 2004AA105030)+1 种基金the Pilot Project of the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KGCX1-21)the National Natural Science Foundation of China (Grant No. 10453001)
文摘This work describes the barometric altimetry as virtual constellation applied to the Chinese Area Positioning System (CAPS), which uses the transponders of communication satellites to transfer navigation messages to users. Barometric altimetry depends on the relationship of air pressure varying with altitude in the Earth’s atmosphere. Once the air pressure at a location is measured the site altitude can be found. This method is able to enhance and improve the availability of three-dimensional positioning. The difficulty is that the relation between barometric pressure and altitude is variable in different areas and under various weather conditions. Hence, in order to obtain higher accuracy, we need to acquire the real-time air pressure corresponding to an altimetric region’s reference height. On the other hand, the altimetry method will be applied to satellite navigation system, but the greatest difficulty lies in how to get the real-time air pressure value at the reference height in the broad areas overlaid by satellite navigation. We propose an innovational method to solve this problem. It is to collect the real-time air pressures and temperatures of the 1860 known-altitude weather observatories over China and around via satellite communication and to carry out time extrapolation forecast uniformly. To reduce data quantity, we first partition the data and encode them and then broadcast these information via navigation message to CAPS users’ receivers. Upon the interpolations being done in receivers, the reference air pressure and temperature at the receiver’s nearby place is derived. Lastly, combing with the receiver-observed real air pressure and temperature, the site’s altitude can be determined. The work is presented in the following aspects: the calculation principle, formulae, data collection, encoding, prediction, interpolation method, navigation message transmission together with errors causes and analyses. The advantages and shortcomings of the technique are discussed at the end.
