An underwater mining navigation method based on an improved particle filter (PF) is proposed to solve the problems of non-Gaussian and intense measurement noise during underwater mining, and a new resampling algorithm is designed, as an improvement, to eliminate the influences of particle degeneration and particle impoverishment of PF. Compared to the resampling algorithms, the proposed algorithm avoids particle impoverishment and improves estimation accuracy. Finally, the estimation accuracies of underwater mining navigation algorithms based on the improved PF and the unscented Kalman filter (UKF) are compared by combining the lake trial data and underwater mining navigation model. The results of simulation experiments manifest that the proposed method has more accurate estimation and remarkable robustness.
ZHANG Zhihui
,
FENG Yingbin
,
LI Zhigang
,
ZHAO Xiaohu
. An underwater mining navigation method based on an improved particle filter[J]. Journal of University of Chinese Academy of Sciences, 2020
, 37(4)
: 507
-515
.
DOI: 10.7523/j.issn.2095-6134.2020.04.010
[1] Yeu T K, Yoon S M, Park S J, et al. Study on underwater navigation of crawler type mining robot[C]//OCEANS 2011. IEEE, 2011:1-6.
[2] Pratama, P S, Kim J W, Kim H K, et al. Path planning algorithm to minimize an overlapped path and turning number for an underwater mining robot[C]//15th International Conference on Control, Automation and Systems (ICCAS). Busan, Korea (South):IEEE, 2015:499-504.
[3] Leonard J J, Bahr A. Autonomous underwater vehicle navigation[M]. Springer Handbook of Ocean Engineering. Springer International Publishing, 2016:663-678.
[4] Liu B, Liu K Z, Wang Y Y, et al. A hybrid deep sea navigation system of LBL/DR integration based on UKF and PSO-SVM[J]. Robot, 2015, 37(5):614-620.
[5] Wang S, Gui W, Chen D. Date fusion of orientation of deep-seabed mining vehicle[J]. Journal of Central South University, 2011, 42(3):337-340.
[6] Doucet A, Godsill S, Andrieu C. On sequential Monte Carlo sampling methods for Bayesian filtering[J]. Statistics and computing, 2000, 10(3):197-208.
[7] Carpenter J, Clifford P, Fearnhead P. Improved particle filter for nonlinear problems[J]. IEE Proceedings-Radar, Sonar and Navigation, 1999, 146(1):2-7.
[8] Yin S, Zhu X, Qiu J, et al. State estimation in nonlinear system using sequential evolutionary filter[J]. IEEE Transactions on Industrial Electronics, 2016, 63(6):3786-3794.
[9] Chen N, Ji H, Gao Y, et al. New box particle filter with improved resampling method and extended inclusion volume criteria for multi-target tracking[J]. Radioengineering, 2018, 27(3):847.
[10] Jouin M, Gouriveau R, Hissel D, et al. Particle filter-based prognostics:review, discussion and perspectives[J]. Mechanical Systems and Signal Processing, 2016, 72:2-31.
[11] Li T, Bolic M, Djuric P M. Resampling methods for particle filtering:classification, implementation, and strategies[J]. IEEE Signal Processing Magazine, 2015, 32(3):70-86.
[12] Li T, Villarrubia G, Sun S, et al. Resampling methods for particle filtering:identical distribution, a new method, and comparable study[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(11):969-984.
[13] Read J, Achutegui K, Míguez J. A distributed particle filter for nonlinear tracking in wireless sensor networks[J]. Signal Processing, 2014, 98:121-134.
[14] Li J, Zhao R, Chen J, et al. Target tracking algorithm based on adaptive strong tracking particle filter[J]. IET Science, Measurement & Technology, 2016, 10(7):704-710.
[15] Wu B F, Jen C L. Particle-filter-based radio localization for mobile robots in the environments with low-density WLAN APs[J]. IEEE Trans Industrial Electronics, 2014, 61(12):6860-6870.
[16] Sonka M, Hlavac V, Boyle R. Image processing, analysis, and machine vision[M]. 4th ed. New York:Springer, 2011.
[17] Chang W, Chen C, Jian Y. Visual tracking in high-dimensional state space by appearance-guided particle filtering[J]. IEEE Transactions on Image Processing, 2008, 17(7):1154-1167.
[18] Yin S, Zhu X. Intelligent particle filter and its application to fault detection of nonlinear system[J]. IEEE Transactions on Industrial Electronics, 2015, 62(6):3852-3861.
