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
Underwater terrain-aided navigation is used to complement the traditional inertial navigation employed by autonomous underwater vehicles during lengthy missions. It can provide fixed estimations by matching real-time ...Underwater terrain-aided navigation is used to complement the traditional inertial navigation employed by autonomous underwater vehicles during lengthy missions. It can provide fixed estimations by matching real-time depth data with a digital terrain map, This study presents the concept of using image processing techniques in the underwater terrain matching process. A traditional gray-scale histogram of an image is enriched by incorporation with spatial information in pixels. Edge comer pixels are then defined and used to construct an edge comer histogram, which employs as a template to scan the digital terrain map and estimate the fixes of the vehicle by searching the correlation peak. Simulations are performed to investigate the robustness of the proposed method, particularly in relation to its sensitivity to background noise, the scale of real-time images, and the travel direction of the vehicle. At an image resolution of 1 m2/pixel, the accuracy of localization is more than 10 meters.展开更多
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.
文摘为完成地形辅助导航系统的传感器选型以及改进地形匹配算法和地形辅助导航系统可靠性的研究,开展了传感器精度对地形匹配概率的影响研究.针对不同的地形,分析了高度表误差、速度误差和航向角误差对地形匹配概率的影响,并根据实际要求通过仿真计算确定地形匹配系统中各传感器的误差范围.利用丘陵、山地和高山地等大量地形数据进行测试验证,仿真结果表明:在地形匹配概率大于90%时,速度误差小于2.2 m/s,航向角误差小于0.6°,高度表噪声标准差小于16.4 m.
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Underwater terrain-aided navigation is used to complement the traditional inertial navigation employed by autonomous underwater vehicles during lengthy missions. It can provide fixed estimations by matching real-time depth data with a digital terrain map, This study presents the concept of using image processing techniques in the underwater terrain matching process. A traditional gray-scale histogram of an image is enriched by incorporation with spatial information in pixels. Edge comer pixels are then defined and used to construct an edge comer histogram, which employs as a template to scan the digital terrain map and estimate the fixes of the vehicle by searching the correlation peak. Simulations are performed to investigate the robustness of the proposed method, particularly in relation to its sensitivity to background noise, the scale of real-time images, and the travel direction of the vehicle. At an image resolution of 1 m2/pixel, the accuracy of localization is more than 10 meters.
