When the initial position error or the altimeter measurement noise is large,the BUAA Inertial Terrain-Aided Navigation (BITAN) algorithm based on extended Kalman filtering can not be located accurately.To solve this p...When the initial position error or the altimeter measurement noise is large,the BUAA Inertial Terrain-Aided Navigation (BITAN) algorithm based on extended Kalman filtering can not be located accurately.To solve this problem,we propose a modified BITAN algorithm based on nonlinear optimal filtering.The posterior probability density correction is obtained by using the prior probability density of the system's state transition model and the most recent observations.Hence,the local unobservable system caused by the measurement equation through terrain linearization is avoided.This algorithm is tested by using the digital elevation model and flight data,and is compared with BITAN.Results show that the accuracy of the proposed algorithm is higher than BITAN,and the robustness of the system is improved.展开更多
In view of the characteristics of underwater navigation, the simulation platform of navigation system for autonomous underwater vehicle has been developed based on Windows platform. The system architecture, net commun...In view of the characteristics of underwater navigation, the simulation platform of navigation system for autonomous underwater vehicle has been developed based on Windows platform. The system architecture, net communication and the information flow are discussed. The methods of software realization and some key techniques of the Vehicle Computer and the Navigation Equipment Computer are introduced in particular. The software design of Terrain Matching Computer is introduced also. The simulation platform is verified and analyzed through simulation. The results show that the architecture of the platform is reasonable and reliable, and the mathematic models and simulation algorithms of sub-systems are also valid and practicable.展开更多
In this paper, the marginal Rao-Blackwellized particle filter （MRBPF）, which fuses the Rao-Blackwellized particle filter （RBPF） algorithm and the marginal particle filter （MPF） algorithm, is presented. The state...In this paper, the marginal Rao-Blackwellized particle filter （MRBPF）, which fuses the Rao-Blackwellized particle filter （RBPF） algorithm and the marginal particle filter （MPF） algorithm, is presented. The state space is divided into linear and non-linear parts, which can be estimated separately by the MPF and the optional Kalman filter. Through simulation in the terrain aided navigation （TAN） domain, it is demonstrated that, compared with the RBPF, the root mean square errors （RMSE） and the error variance of the nonlinear state estimations by the proposed MRBPF are respectively reduced by 29% and 96%, while the unique particle count is increased by 80%. It is also found that the MRBPF has better convergence properties, and analysis has shown that the existing RBPF is nothing more than a special case of the MRBPF.展开更多
基金Supported by the National Natural Nature Science Foundation of China （Grant No. 41376102）, Fundamental Research Funds for the Central Universities （Gant No. HEUCF150514） and Chinese Scholarship Council （Grant No. 201406680029）.
文摘在水下帮助地面的航行被用来补充采用由的传统的惯性的航行自治在水下车辆在过长的使命期间。它能由与一张数字地面地图匹配即时深度数据提供固定评价。这研究介绍使用处理技术在的图象的概念在水下地面匹配过程。一幅图象的一张传统的灰阶的直方图被加入在象素与空间信息充实。边角落象素然后被定义并且过去常构造一张边角落直方图，扫描数字地面地图并且由寻找关联山峰估计车辆的改正作为一个模板采用它。模拟被执行调查建议方法的坚韧性，特别地在与它到背景噪音，即时图象的规模，和车辆的旅行方向的敏感的关系。在 1 m <sup>2</sup>/pixel, 的一个图象决定，本地化的精确性是超过 10 米。
基金supported by the National Natural Science Foundation of China (Grant No.61039003)the Aeronautical Science Foundation of China (Grant Nos.20090818004 and 20100851018)the National Key Laboratory Foundation
文摘When the initial position error or the altimeter measurement noise is large,the BUAA Inertial Terrain-Aided Navigation (BITAN) algorithm based on extended Kalman filtering can not be located accurately.To solve this problem,we propose a modified BITAN algorithm based on nonlinear optimal filtering.The posterior probability density correction is obtained by using the prior probability density of the system's state transition model and the most recent observations.Hence,the local unobservable system caused by the measurement equation through terrain linearization is avoided.This algorithm is tested by using the digital elevation model and flight data,and is compared with BITAN.Results show that the accuracy of the proposed algorithm is higher than BITAN,and the robustness of the system is improved.
文摘In view of the characteristics of underwater navigation, the simulation platform of navigation system for autonomous underwater vehicle has been developed based on Windows platform. The system architecture, net communication and the information flow are discussed. The methods of software realization and some key techniques of the Vehicle Computer and the Navigation Equipment Computer are introduced in particular. The software design of Terrain Matching Computer is introduced also. The simulation platform is verified and analyzed through simulation. The results show that the architecture of the platform is reasonable and reliable, and the mathematic models and simulation algorithms of sub-systems are also valid and practicable.
基金National Natural Science Foundation of China （60572023）
文摘In this paper, the marginal Rao-Blackwellized particle filter （MRBPF）, which fuses the Rao-Blackwellized particle filter （RBPF） algorithm and the marginal particle filter （MPF） algorithm, is presented. The state space is divided into linear and non-linear parts, which can be estimated separately by the MPF and the optional Kalman filter. Through simulation in the terrain aided navigation （TAN） domain, it is demonstrated that, compared with the RBPF, the root mean square errors （RMSE） and the error variance of the nonlinear state estimations by the proposed MRBPF are respectively reduced by 29% and 96%, while the unique particle count is increased by 80%. It is also found that the MRBPF has better convergence properties, and analysis has shown that the existing RBPF is nothing more than a special case of the MRBPF.
基金This work was supported by the National Key Basic Research and Development （973） Program of China （Grant No. 2010CB731806） and Aeronautical Science Foundation of China （Grant No. 20100818018）.
