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A Passive Acoustic Positioning Algorithm Based on Virtual Long Baseline Matrix Window

  • Tao Zhang (a1), Ziqiang Wang (a1), Yao Li (a1) and Jinwu Tong (a1)

Abstract

A new acoustic positioning method for Autonomous Underwater Vehicles (AUV) that uses a single underwater hydrophone is proposed in this paper to solve problems of Long Baseline (LBL) array laying and communication synchronisation problems among all hydrophones in the traditional method. The proposed system comprises a Strapdown Inertial Navigation System (SINS), a single hydrophone installed at the bottom of the AUV and a single underwater sound source that emits signals periodically. A matrix of several virtual hydrophones is formed with the movement of the AUV. In every virtual LBL window, the time difference from the transmitted sound source to each virtual hydrophone is obtained by means of a Smooth Coherent Transformation (SCOT) weighting cross-correlation in the frequency domain. Then, the recent location of the AUV can be calculated. Simulation results indicate that the proposed method can effectively compensate for the position error of SINS. Thus, the positioning accuracy can be confined to 2 m, and the method achieves good applicability. Compared with traditional underwater acoustic positioning systems, the proposed method can provide great convenience in engineering implementation and can reduce costs.

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An, L., Chen, L. J. and Fang, S. L. (2013). Investigation on Correlation Peaks Ambiguity and Ambiguity Elimination Algorithm in Underwater Acoustic Passive Localization. Journal of Electronics & Information Technology, 35(12), 29482953.
Barisic, M., Miskovic, N. and Vasilijevic, A. (2012). Fusing Hydroacoustic Absolute Position Fixes With AUV On-Board Dead Reckoning. IFAC Proceedings Volumes, 45(22), 211217.
Casalino, A. T. G., Simetti, E., Sperindè, A. and Torelli, S. (2014). Impact of LBL Calibration on the Accuracy of Underwater Localization. IFAC Proceedings Volumes, 47(3), 33763381.
Choi, Y. H., Lee, J. W., Hong, S. H., Suh, J. H. and Kim, J. G. (2015). The development of the modular autonomous underwater navigation system based on OPRoS. International Conference on Ubiquitous Robots and Ambient Intelligence. IEEE, 625628.
Cohen, L. (1998). The generalization of the Wiener-Khinchin theorem. IEEE Signal Processing Letters, 5(11), 292294.
Deng, A. D., Bao, Y. Q. and Zhao, L. (2009). Research on Time Delay Estimation Algorithm Based on Generalized Cross Correlation in Acoustic Emission Source Location. Proceedings of the CSEE, 29(14), 8692.
Donovan, G. T. (2012). Position Error Correction for an Autonomous Underwater Vehicle Inertial Navigation System (INS) Using a Particle Filter. IEEE Journal of Oceanic Engineering, 37(3), 431445.
Ferreira, B., Matos, A. and Cruz, N. (2010). Estimation approach for AUV navigation using a single acoustic beacon. Sea Technology, 51(12), 5459.
Ji, C. L., Zhang, N., Wang, H. H. and Zheng, C. E. (2014). Application of Kalman Filter in AUV Acoustic Navigation. Applied Mechanics & Materials, 525, 695701.
Ji, D. X., Song, W., Zhao, H. Y. and Liu, J. (2016). Deep Sea AUV Navigation Using Multiple Acoustic Beacons. China Ocean Engineering (English Edition), 30(2), 309318.
Jiao, X. T., Li, J. C. and Men, L. J. (2013). Accuracy Analysis of Two TDOA Algorithms in Passive Underwater Acoustic Positioning. Audio Engineering, 37(1), 7375.
Lee, P. M., Huan, J. B., Choi, H. T. and Hong, S. W. (2005). An integrated navigation systems for underwater vehicles based on inertial sensors and pseudo LBL acoustic transponders. Oceans. Washington, DC, USA, IEEE, 1, 555562.
Mahdinejad, K. and Seghaleh, M. Z. (2013). Implementation of time delay estimation using different weighted generalized cross correlation in room acoustic environments. Life Science Journal, 10(6S), 846851.
Maki, T., Matsuda, T., Sakamaki, T., Ura, T. and Kojima, J. (2013). Navigation Method for Underwater Vehicles Based on Mutual Acoustical Positioning With a Single Seafloor Station. IEEE Journal of Oceanic Engineering, 38(1), 167177.
Michael, B. P. (2011). The BELLHOP Manual and User's Guide: PRELIMINARY DRAFT. Heat, Light, and Sound Research, Inc.
Miller, P. A., Farrell, J. A., Zhao, Y. Y. and Djapic, V. (2010). Autonomous underwater vehicle navigation. IEEE Journal of Oceanic Engineering, 35(3), 663678.
Morgado, M., Batista, P., Oliveira, P. and Silvestre, V. (2010). Position USBL/DVL sensor-based navigation filter in the presence of unknown ocean currents. Automatica, 47(12), 26042614.
Ngatini, , Apriliani, E. and Nurhadi, H. (2016). Ensemble and Fuzzy Kalman Filter for Position Estimation of an Autonomous Underwater Vehicle Based on Dynamical System of AUV Motion. Expert Systems with Applications, 68, 2935.
Paull, L., Saeedim S., Seto, M. and Li, H. (2014). AUV Navigation and Localization: A Review. IEEE Journal of Oceanic Engineering, 39(1), 131149.
Yu, P. and Wu, B. (2015). The optimal design of long baseline acoustic positioning array. Ship Electronic Engineering, 35(5), 125129.
Zhang, J. C., Han, Y. F., Zheng, C. and Sun, D. (2016a). Underwater target localization using long baseline positioning system. Applied Acoustics, 111, 129134.
Zhang, T., Chen, L. P. and Li, Y. (2015). AUV Underwater Positioning Algorithm Based on Interactive Assistance of SINS and LBL. Sensors, 16(1), 4264.
Zhang, T., Shi, H. F., Chen, L. P., Li, Y. and Tong, J. W. (2016b). AUV Positioning Method Based on Tightly Coupled SINS/LBL for Underwater Acoustic Multipath Propagation. Sensors, 16(3), 357373.
Zhang, T., Xu, X., Li, Y. and Gong, S. P. (2013). AUV fault tolerant navigation technology based on ins and underwater acoustic assistance system. Chinese Journal of Inertial Technology, 21(4), 512516.
Zhong, S., Xia, W. and He, Z. S. (2015). Multipath Time Delay Estimation Based on Gibbs Sampling under Incoherent Reception Environment. IEICE Transactions on Fundamentals of Electronics Communications & Computer Sciences, E98.A(6), 13001304.

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