An Efficient Positioning and Tracking Algorithm for AUV in Underwater Environment

Xiaohong Tong; Chao Tang

doi:10.6180/jase.201803_21(1).0010

An Efficient Positioning and Tracking Algorithm for AUV in Underwater Environment

Computer Science and Information Engineering

Xiaohong Tong This email address is being protected from spambots. You need JavaScript enabled to view it.¹ and Chao Tang²

¹Information Center, Hefei Technology College, Hefei 238000, P.R. China
²Department of Computer Science and Technology, Hefei University, Hefei 230601, P.R. China

Received: April 24, 2017
Accepted: January 6, 2018
Publication Date: March 1, 2018

Download Citation: ||https://doi.org/10.6180/jase.201803_21(1).0010

ABSTRACT

Autonomous underwater vehicle (AUV) research has focused on tracking, positioning, precise guidance, return to dock, and other tasks. The AUV called a robotic fish has become a hot research topic in several areas, including for intelligent education, civil and military uses. In the nonlinear tracking analysis of the robotic fish, it was found that the interval Kalman filtering algorithm contains all possible filtered results but that the range is wide and relatively conservative, and the interval data vector is uncertain. This paper proposes an optimized algorithm for suboptimal interval Kalman filtering. The suboptimal interval Kalman filtering scheme uses the interval inverse matrix with its lowest inverse. This proposed method provides a more approximate nonlinear state equation and measurement equation than does the standard interval Kalman filtering, increases the accuracy of the nominal dynamic system model, and improves the speed and precision of the tracking system. Monte-Carlo simulation results show that the optimal trajectory of the suboptimal interval Kalman filtering algorithm is better than that of both the interval Kalman filtering method and the standard filtering method.

Keywords: AUV, Kalman Filtering, Suboptimal Interval Kalman Filtering, Robotic Fish Tracking, Monte-Carlo Simulation

REFERENCES

[1] Shojaei, K. and Dolatshahi, M., “Line-of-sight Target Tracking Control of Underactuated Autonomous Underwater Vehicles,” Ocean Engineering, Vol. 133, pp. 244252 (2017). doi: 10.1016/j.oceaneng.2017.02.007
[2] Koca, G. O., Korkmaz, D., Bal, C., Akpolat, Z. H. and Ay, M., “Implementations of the Route Planning Scenarios for the Autonomous Robotic Fish with the Optimized Propulsion Mechanism,” Measurement, Vol. 93, pp. 232242 (2016). doi: 10.1016/j.measurement. 2016.07.026
[3] Suebsaiprom, P., Lin, C. L. and Engkaninan, A., “Undulatory Locomotion and Effective Propulsion for Fish-inspired Robot,” Control Engineering Practice, Vol. 58, pp. 6677 (2017). doi: 10.1016/j.conengprac. 2016.09.007
[4] Suebsaiprom, P. and Lin, C. L., “Maneuverability Modeling and Trajectory Tracking for Fish Robot,” Control Engineering Practice, Vol. 45, pp. 2236 (2015). doi: 10.1016/j.conengprac.2015.08.010
[5] Kennedy, H. L., “Mixture Reduction Techniques and Probabilistic Intensity Models for Multiple Hypothesis Tracking of Targets in Clutter,” Computers & Electrical Engineering, Vol. 40, No. 3, pp. 884896 (2014). doi: 10.1016/j.compeleceng.2013.07.023
[6] Jiang, X., Harishan, K., Tharmarasa, R., Kirubarajan, T. and Thayaparan, T., “Integrated Track Initialization and Maintenance in Heavy Clutter Using Probabilistic Data Association,” Signal Processing, Vol. 94, No. 1, pp. 241250 (2014). doi: 10.1016/j.sigpro.2013.06.026
[7] Ngatini, Apriliani, E. and Nurhadi, H., “Ensemble and Fuzzy Kalman Filter for Position Estimation of an Autonomous Underwater Vehicle Based on Dynamical System of Auv Motion,” Expert Systems with Applications, Vol. 68, pp. 2935 (2016). doi: 10.1016/j.eswa. 2016.10.003
[8] Senanayake, M.,Senthooran, I., Barca,J.C.,Murshed, M., Murshed, M. and Murshed, M., “Search and Tracking Algorithms for Swarms of Robots,” Robotics & Autonomous Systems, Vol. 75, pp. 422434 (2016). doi: 10.1016/j.robot.2015.08.010
[9] Yadav, A. K., Mishra, V. K., Singh, A. K. and Bhaumik, S., “Unscented Kalman Filter for Arbitrary Step Randomly Delayed Measurements,” Control Conference, IEEE Press, pp. 7846456 (2017). doi: 10.1109/ INDIANCC.2017.7846456
[10] Belik, B. V. and Belov, S. G, “Using of Extended Kalman Filter for Mobile Target Tracking in the Passive Air Based Radar System,” Procedia Computer Science, Vol. 103, pp. 280286 (2017). doi: 10.1016/j.procs. 2017.01.108
[11] Allik, B., Miller, C., Piovoso, M. J. and Zurakowski, R, “Nonlinear Estimators for Censored Data: a Comparison of the EKF, the UKF and the Tobit KalmanFilter,” American Control Conference, IEEE Press, Vol. 2015, pp. 51465151 (2015). doi: 10.1109/ACC.2015. 7172142
[12] Duan, J. M., Liu, D., Yu, H. X. and Shi, H., “An Improved Fast SLAM Algorithm for Autonomous Vehicle Based on the Strong Tracking Square Root Central Difference Kalman Filter,” International Conference on Intelligent Transportation Systems, IEEE Computer Society Press, pp. 693698 (2015). doi: 10.1109/ITSC. 2015.118
[13] Miao, Z. Y., Lv, Y. L., Xu, D. J., Shen, F. and Pang, S. W., “Analysis of a Variational Bayesian Adaptive Cubature Kalman Filter Tracking Loop for High Dynamic Conditions,” GPS Solutions, Vol. 21 No. 3, pp. 111 122 (2017). doi: 10.1007/s10291-015-0510-0
[14] Allotta, B., Caiti, A., Chisci, L., Costanzi, R., Corato, F. D. and Fantacci, C., et al., “An Unscented Kalman Filter Based Navigation Algorithm for Autonomous Underwater Vehicles,” Mechatronics, Vol. 39, pp. 185 195 (2016). doi: 10.1016/j.mechatronics.2016.05.007
[15] Erazo, K. and Nagarajaiah, S., “An Offline Approach for Output-only Bayesian Identification of Stochastic Nonlinear Systems Using Unscented Kalman Filtering,” Journal of Sound & Vibration, Vol. 397, pp. 222240 (2017). doi: 10.1016/j.jsv.2017.03.001
[16] Siouris, G. M., Chen, G. and Wang, J., “Tracking an Incoming Ballistic Missile Using an Extended Interval Kalman Filter,” IEEE Transactions on Aerospace & Electronic Systems, Vol. 33, No. 1, pp. 232240 (2002). doi: 10.1109/7.570753
[17] Motwani, A., Sharma, S. K., Sutton, R. and Culverhouse, P., “Interval Kalman Filtering in Navigation System Design for an Uninhabited Surface Vehicle,” Journal of Navigation,Vol. 66, No. 5, pp. 639652 (2013). doi: 10.1017/S0373463313000283
[18] Clark, D., Ruiz, I. T., Petillot, Y. and Bell, J., “Particle phd Filter Multiple Target Tracking in Sonar Image,” IEEE Transactions on Aerospace & Electronic Systems, Vol. 43, No. 1, pp. 409416 (2007). doi: 10. 1109/TAES.2007.357143
[19] Ma,T., Zhao, X., Gao, B., etal., “Combined Shape and Topology Optimization of Free-form Structure,” Journal of Zhejiang University (Engineering Edition), Vol. 49, No. 10, pp. 19461951 (2015). (Chinese)
[20] Xiang, X., Yu, C. and Zhang, Q., “Robust Fuzzy 3d Path Following for Autonomous Underwater Vehicle Subject to Uncertainties,” Computers & Operations Research, Vol. 84, pp. 165–177 (2016). doi: 10.1016/ j.cor.2016.09.017
[21] Aghajani, Aydin, et al., “AMulti-objective Mathematical Model for Cellular Manufacturing Systems Design with Probabilistic Demand and Machine Reliability Analysis,” International Journal of Advanced Manufacturing Technology, Vol. 75, No. 58, pp. 755770 (2014). doi: 10.1007/s00170-014-6084-0
[22] Crasta, N., Bayat, M., Aguiar, A. P. and Pascoal, A. M., “Observability Analysis of 3d auv Trimming Trajectories in the Presence of Ocean Currents Using Range and Depth Measurements,” Annual Reviews in Control, Vol. 40, pp. 142156 (2015). doi: 10.1016/j. arcontrol.2015.09.009
[23] Conti, R., Fanelli, F., Meli, E., Ridolfi, A. and Costanzi, R., “AFree Floating Manipulation Strategy for Autonomous Underwater Vehicles,” Robotics & Autonomous Systems, Vol. 87, pp. 133146 (2016). doi: 10.1016/ j.robot.2016.09.018
[24] Jhang, J., Zhou, J. and Lu, X. B., “Flocking Analysis and Comparison in Simplex Multi-agent Systems under Matrix-weighting Hölder Norms,” IFAC-Papers on Line, Vol. 48, No. 28, pp. 438443 (2015). doi: 10.1016/j.ifacol.2015.12.167
[25] Ding, F., Wang, X., Mao, L. and Xu, L., “Joint State and Multi-innovation Parameter Estimation for Time delay Linear Systems and Its Convergence Based on the Kalman Filtering,” Digital Signal Processing, Vol. 62, pp. 211223 (2017). doi: 10.1016/j.dsp.2016.11.010
[26] Wieringen, W. N. V., “On the Mean Squared Error of the Ridge Estimator of the Covariance and Precision Matrix,” Statistics & Probability Letters, Vol. 123, pp. 8892 (2017). doi: 10.1016/j.spl.2016.12.002
[27] Chen, C. Y., Chen, C. H., Chen, C. H. and Lin, K. P., “An Automatic Filtering Convergence Method for Iterative Impulse Noise Filters Based on psnr Checking and Filtered Pixels Detection,” Expert Systems with Applications, Vol. 63, pp. 198207 (2016). doi: 10. 1016/j.eswa.2016.07.003
[28] He, Y., He, Z., Lee, D. H., Kim, K. J., Zhang, L. and Yang, X., “Robust Fuzzy Programming Method for mro Problems Considering Location Effect, Dispersion Effect and Model Uncertainty,” Computers & Industrial Engineering, Vol. 105, pp. 7683 (2017). doi: 10.1016/j.cie.2016.12.021
[29] Allotta, B., Caiti, A., Chisci, L., Costanzi, R., Corato, F. D. and Fantacci, C., et al., “An Unscented Kalman Filter Based Navigation Algorithm for Autonomous Underwater Vehicles,” Mechatronics, Vol. 39, pp. 185 195 (2016). doi: 10.1016/j.mechatronics.2016.05.007
[30] Fornberg, B., “Fast Calculation of Laurent Expansions for Matrix Inverses,” Journal of Computational Physics, Vol. 326, pp. 722732 (2016). doi: 10.1016/j.jcp.2016.09.028
[31] Ou, H. F., Zhang, B. and Zhao, S. J., “Monte Carlo Simulation for Calculation of Fragments Produced by 400 mev/uCarbon Ion Beamin Water,” Nuclear Inst & Methods in Physics Research B, Vol. 396, pp. 1825 (2017). doi: 10.1016/j.nimb.2017.01.077
[32] Øyvind Ødegård, Sørensen, A. J., Hansen, R. E. and Ludvigsen, M., “A New Method for Underwater Archaeological Surveying Using Sensors and Unmanned Platforms,” IFAC-Papers on Line, Vol. 49, No. 23, pp. 486493 (2016). doi: 10.1016/j.ifacol.2016.10.453
[33] Zheng, W., Zou, Q. and Ni, W., “Design and Application of Underwater Unmanned Vehicle Simulation System for Navigation and Obstacle Avoidance,” Journal of System Simulation, Vol. 28, No. 1, pp. 9198 (2016). (Chinese)