The integrated navigation system with strapdown inertial navigation system (SINS), Beidou (BD) receiver and Doppler velocity log (DVL) can be used in marine applications owing to the fact that the redundant and complementary information from different sensors can markedly improve the system accuracy. However, the existence of multisensor asynchrony will introduce errors into the system. In order to deal with the problem, conventionally the sampling interval is subdivided, which increases the computational complexity. In this paper, an innovative integrated navigation algorithm based on a Cubature Kalman filter (CKF) is proposed correspondingly. A nonlinear system model and observation model for the SINS/BD/DVL integrated system are established to more accurately describe the system. By taking multi-sensor asynchronization into account, a new sampling principle is proposed to make the best use of each sensor’s information. Further, CKF is introduced in this new algorithm to enable the improvement of the filtering accuracy. The performance of this new algorithm has been examined through numerical simulations. The results have shown that the positional error can be effectively reduced with the new integrated navigation algorithm. Compared with the traditional algorithm based on EKF, the accuracy of the SINS/BD/DVL integrated navigation system is improved, making the proposed nonlinear integrated navigation algorithm feasible and efficient.
References
[1]
Lupton, T. Inertial SLAM with Delayed Initialisation. Ph.D. Thesis, Department Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, Australia, March 2010.
[2]
Allotta, B.; Pugi, L.; Costanzi, R.; Vettori, G. Localization Algorithm for a Fleet of Three AUVs by INS, DVL and Range Measurements. Proceedings of the 15th International Conference on Advanced Robotics, Tallinn, Estonia, 20–23 June 2011; pp. 978–981.
[3]
Einicke, G.A.; Falco, G.; Malos, J.T. Bounded constrained filtering for GPS/INS integration. IEEE Trans. Autom. Control 2013, 58, 125–133.
[4]
Yuan, G.N.; Yuan, K.F.; Zhang, H.W. A Variable Proportion Adaptive Federal Kalman Filter for INS/ESGM/GPS/DVL Integrated Navigation System. Proceedings of the 4th International Joint Conference on Computational Sciences and Optimization, Kunming, China, 15–19 April 2011; pp. 978–981.
[5]
Donovan, G.T. Position error correction for an autonomous underwater vehicle inertial navigation system using a particle filter. IEEE J. Ocean. Eng. 2012, 37, 125–133.
[6]
Chen, C.H.; Zhao, X.L. Simulation Analysis of Positioning Performance of BeiDou-2 and Integrated BeiDou-2/GPS. Proceedings of 2010 International Conference on Communications and Mobile Computing, Shenzhen, China, 12–14 April 2010; pp. 505–509.
[7]
Truong, D.M.; Tran, T.T.; Nguyen, T.D.; Ta, T.H. Recent Results in Receiving and Decoding Signals from the Beidou System. Proceedings of 2013 International Conference on Localization and GNSS (ICL-GNSS), Turin, Italy, 25–27 June 2013; pp. 1–4.
[8]
Chen, C.H.; Zhao, X.L. Simulation Analysis of Positioning Performance of BeiDou-2 Satellite Navigation System. Proceedings of 2010 2nd International Conference on Advance Computer Control, Shenyang, China, 27–29 March 2010; pp. 148–152.
[9]
Hua, B.; Liu, J.Y.; Xiong, Z.; Zhu, Y.H. Federal filtering algorithm in SINS/Beidou/STAR intehrated navigation system. J. Appl. Sci. 2006, 24, 120–124.
[10]
Huang, X.F.; Wu, Q.Z. An Algorithm of Weighted Covariance for Centralized Asynchronous Fusion Based on Kalman. Proceedings of the 2012 Internation Confernce on Industrial Control and Electronics Engineering, Xi'an, China, 23–25 August 2012; pp. 1554–1557.
[11]
Luo, C.; McClean, S.I.; Parr, G.; Teacy, L.; Nardi, R.D. UAV position estimation and collision avoidance using the extended Kalman filter. IEEE Trans. Veh. Technol. 2013, 62, 2749–2762.
[12]
Suranthiran, S.; Jayasuriya, S. Optimal fusion of multiple nonlinear sensor data. IEEE Sens. J. 2004, 4, 651–663.
[13]
Zhang, X.H.; Guo, H.D.; Xia, Z.J. An Asynchronous Multisensory Spatial Registration Algorithm. Proceedings of the Fourth International Conference on Fuzzy System and Knowledge Discovery, Haikou, China, 24–27 August 2007; pp. 16–20.
[14]
Lin, C.M.; Hsueh, C.S. Adaptive EKF-CMAC-based multisensory data fusion for maneuvering target. IEEE Trans. Instrum. Meas. 2013, 62, 2058–2066.
[15]
Sun, G.H.; Wang, M.; Wu, L.G. Unexpected results of extended fractional kalman filter for parameters identification in fractional order chaotic systems. Int. J. Innov. Comput. 2011, 7, 5341–5352.
[16]
Rashid, U.; Tuan, H.D.; Apkarian, P.; Kha, H. Globally optimized power allocation in multiple sensor fusion for linear and nonlinear networks. IEEE Trans. Signal Process. 2012, 60, 903–915.
[17]
Gundimada, S.; Asari, V.K. Facial recognition using multisensor images based on localized Kernel Eigen spaces. IEEE Trans. Image Process. 2009, 18, 1314–1325.
[18]
Hu, H.D.; Huang, X.L. SINS/CNS/GPS integrated navigation algorithm based on UKF. J. Syst. Eng. Electron. 2010, 21, 102–109.
[19]
Arasaeatnam, I.; Haykin, S. Cubature Kalman filters. IEEE Trans. Autom. Control 2009, 54, 1254–1269.
[20]
Arasaeatnam, I.; Haykin, S.; Hurd, T.R. Cubature Kalman filters for continuous-discrete system: Theory and simulations. IEEE Trans. Signal Process. 2010, 58, 4977–4993.
[21]
Pakki, K.; Chandra, B.; Gu, D.W.; Postlethwaite, I. Cubature Information Filter and Its Applications. Proceedings of the 2011 American Control Conference, San Francisco, CA, USA, 29 June–1 July 2011; pp. 3609–3614.
[22]
Arasaeatnam, I.; Haykin, S. Cubature Kalman smoothers. J. Autom. 2011, 47, 2245–2250.
[23]
Zhou, B.C.; Cheng, X.H. Federated Filtering Algorithm Based on Fuzzy Adaptive UKF for Marine SINS/GPS/DVL Integrated System. Proceedings of the 2010 Chinese Controland Decision Conference (CCDC 2010), Xuzhou, China, 26–28 May 2010; pp. 2082–2085.
