China has developed an airborne gravimetry system based on SINS/DGPS named SGA-WZ, the first system in which a strap- down inertial navigation system （SINS） has been used for airborne gravimetry in China. This gravi...China has developed an airborne gravimetry system based on SINS/DGPS named SGA-WZ, the first system in which a strap- down inertial navigation system （SINS） has been used for airborne gravimetry in China. This gravity measurement system consists of a strap-down inertial navigation system and a differential global positioning system （DGPS）. In April 2010, a flight test was carried out in Shandong Province of China to test the accuracy of this system. The test was designed to assess the re- peatability and accuracy of the system. Two repeated flights and six grid flights were made. The flying altitude was about 400 m. The average flying speed was about 60 m/s, which corresponds to a spatial resolution of 4.8 km when using 160-s cutoff low-pass filter. This paper describes the data processing of the system. The evaluation of the internal precision is based on repeated flights and differences in crossover points. Gravity results in this test from the repeated flight lines show that the re- peatability of the repeat lines is 1.6 mGal with a spatial resolution of 4.8 kin, and the internal precision of grid flight data is 3.2 mGal with a spatial resolution of 4.8 km. There are some systematic errors in the gravity results, which can be modeled using trigonometric function. After the systematic errors are compensated, the precision of grid flight data can be better than 1 mGal.展开更多
Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobil...Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.展开更多
WiFi fingerprinting is the method of recording WiFi signal strength from access points （AP） along with the positions at which they were recorded, and later matching those to new mea- surements for indoor positioning...WiFi fingerprinting is the method of recording WiFi signal strength from access points （AP） along with the positions at which they were recorded, and later matching those to new mea- surements for indoor positioning. Inertial positioning utilizes the accelerometer and gyroscopes for pedestrian positioning. However, both methods have their limitations, such as the WiFi fluctuations and the accumulative error of inertial sensors. Usually, the filtering method is used for integrating the two approaches to achieve better location accuracy. In the real environments, especially in the indoor field, the APs could be sparse and short range. To overcome the limitations, a novel particle filter approach based on Rao Blackwellized particle filter （RBPF） is presented in this paper. The indoor environment is divided into several local maps, which are assumed to be independent of each other. The local areas are estimated by the local particle filter, whereas the global areas are com- bined by the global particle filter. The algorithm has been investigated by real field trials using a WiFi tablet on hand with an inertial sensor on foot. It could be concluded that the proposed method reduces the complexity of the positioning algorithm obviously, as well as offers a significant improvement in position accuracy compared to other conventional algorithms, allowing indoor positioning error below 1.2 m.展开更多
High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based...High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.展开更多
基金supported by the National High-Tech Research&Development Program of China(Grant No.2006AA06A202)the Youth Innovation Foundation of China Aero Geophysical Survey&Remote Sensing Center for Land and Resources(Grant No.2010YFL05)
文摘China has developed an airborne gravimetry system based on SINS/DGPS named SGA-WZ, the first system in which a strap- down inertial navigation system （SINS） has been used for airborne gravimetry in China. This gravity measurement system consists of a strap-down inertial navigation system and a differential global positioning system （DGPS）. In April 2010, a flight test was carried out in Shandong Province of China to test the accuracy of this system. The test was designed to assess the re- peatability and accuracy of the system. Two repeated flights and six grid flights were made. The flying altitude was about 400 m. The average flying speed was about 60 m/s, which corresponds to a spatial resolution of 4.8 km when using 160-s cutoff low-pass filter. This paper describes the data processing of the system. The evaluation of the internal precision is based on repeated flights and differences in crossover points. Gravity results in this test from the repeated flight lines show that the re- peatability of the repeat lines is 1.6 mGal with a spatial resolution of 4.8 kin, and the internal precision of grid flight data is 3.2 mGal with a spatial resolution of 4.8 km. There are some systematic errors in the gravity results, which can be modeled using trigonometric function. After the systematic errors are compensated, the precision of grid flight data can be better than 1 mGal.
基金Project(2013AA06A411)supported by the National High Technology Research and Development Program of ChinaProject(CXZZ14_1374)supported by the Graduate Education Innovation Program of Jiangsu Province,ChinaProject supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions,China
文摘Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.
文摘WiFi fingerprinting is the method of recording WiFi signal strength from access points （AP） along with the positions at which they were recorded, and later matching those to new mea- surements for indoor positioning. Inertial positioning utilizes the accelerometer and gyroscopes for pedestrian positioning. However, both methods have their limitations, such as the WiFi fluctuations and the accumulative error of inertial sensors. Usually, the filtering method is used for integrating the two approaches to achieve better location accuracy. In the real environments, especially in the indoor field, the APs could be sparse and short range. To overcome the limitations, a novel particle filter approach based on Rao Blackwellized particle filter （RBPF） is presented in this paper. The indoor environment is divided into several local maps, which are assumed to be independent of each other. The local areas are estimated by the local particle filter, whereas the global areas are com- bined by the global particle filter. The algorithm has been investigated by real field trials using a WiFi tablet on hand with an inertial sensor on foot. It could be concluded that the proposed method reduces the complexity of the positioning algorithm obviously, as well as offers a significant improvement in position accuracy compared to other conventional algorithms, allowing indoor positioning error below 1.2 m.
基金supported in part by the National Natural Science Foundation of China under Grant No.61771474in part by the Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant No.KYCX212243+2 种基金in part by the Young Talents of Xuzhou Science and Technology Plan Project under Grant No.KC19051in part by the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University under Grant No.2021D02in part by the Open Fund of Information Photonics and Optical Communications (IPOC) (BUPT)。
文摘High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.
