Li Q.,Nanjing Southeast University |
Li Q.,Shandong Polytechnic University |
Chen X.,Nanjing Southeast University |
Chen X.,Key Laboratory of Micro inertial Instrument and Advanced Navigation Technology of Ministry of Education |
And 2 more authors.
Journal of Southeast University (English Edition) | Year: 2012
In order to keep stable navigation accuracy when the blind node (BN) moves between two adjacent clusters, a distributed fusion method for the integration of the inertial navigation system (INS) and the wireless sensor network (WSN) based on H ∞ filtering is proposed. Since the process and measurement noise in the integration system are bounded and their statistical characteristics are unknown, the H ∞ filter is used to fuse the information measured from local estimators in the proposed method. Meanwhile, the filter can yield the optimal state estimate according to certain information fusion criteria. Simulation results show that compared with the federal Kalman solution, the proposed method can reduce the mean error of position by about 45% and the mean error of velocity by about 85%. Single ©, including postage.
Cheng X.-H.,Key Laboratory of Micro inertial Instrument and Advanced Navigation Technology of Ministry of Education |
Cheng X.-H.,Nanjing Southeast University |
Han X.,Key Laboratory of Micro inertial Instrument and Advanced Navigation Technology of Ministry of Education |
Han X.,Nanjing Southeast University |
And 4 more authors.
Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology | Year: 2015
A transfer alignment method based on star sensors in the launch-point inertial coordinate system is proposed aiming at solving the problem of rapid response and precise transfer alignment of near space vehicles. The nonlinear transfer alignment models based on misalignment angle and additive quaternion are established according to the different forms of attitude information from star sensors, such as the attitude angle by TRIAD algorithm or the attitude quaternion by QUEST algorithm. Mathematical simulation is conducted by applying UKF to deal with the nonlinear feature of the transfer alignment model, and the results show that three attitude angles of the two transfer alignment algorithms can all converge to 20″ within 2 s. The simulation verifies the effectiveness of the two algorithms and provides a reference for the transfer alignment of near space vehicles. ©, 2015, Editorial Department of Journal of Chinese Inertial Technology. All right reserved.
Liu X.,Nanjing Southeast University |
Liu X.,Key Laboratory of Micro inertial Instrument and Advanced Navigation Technology of Ministry of Education |
Xu X.,Nanjing Southeast University |
Xu X.,Key Laboratory of Micro inertial Instrument and Advanced Navigation Technology of Ministry of Education |
And 4 more authors.
Mathematical Problems in Engineering | Year: 2014
In the initial alignment process of strapdown inertial navigation system (SINS), large initial misalignment angles always bring nonlinear problem, which causes alignment failure when the classical linear error model and standard Kalman filter are used. In this paper, the problem of large misalignment angles in SINS initial alignment is investigated, and the key reason for alignment failure is given as the state covariance from Kalman filter cannot represent the true one during the steady filtering process. According to the analysis, an alignment method for SINS based on multiresetting the state covariance matrix of Kalman filter is designed to deal with large initial misalignment angles, in which classical linear error model and standard Kalman filter are used, but the state covariance matrix should be multireset before the steady process until large misalignment angles are decreased to small ones. The performance of the proposed method is evaluated by simulation and car test, and the results indicate that the proposed method can fulfill initial alignment with large misalignment angles effectively and the alignment accuracy of the proposed method is as precise as that of alignment with small misalignment angles. © 2014 Xixiang Liu et al.