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An Improved ICCP Matching Algorithm for use in an Interference Environment during Geomagnetic Navigation

Published online by Cambridge University Press:  10 July 2019

Jing Xiao ,
Xiusheng Duan ,
Xiaohui Qi  and
Yifei Liu
Jing Xiao
Affiliation:
(Northwest Institute of Nuclear Technology, No. 28, Pingyu Road, Xi'an 710024, PR China)
Xiusheng Duan*
Affiliation:
(Shijiazhuang Tiedao University, No. 17, North Second Ring Road, Shijiazhuang 050043, PR China)
Xiaohui Qi
Affiliation:
(Army Engineering University, No. 97, Hepingxi Road, Shijiazhuang 050003, PR China)
Yifei Liu
Affiliation:
(Northwest Institute of Nuclear Technology, No. 28, Pingyu Road, Xi'an 710024, PR China)
*
(E-mail: sjzdxsh@163.com)
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Abstract

The Iterated Closest Contour Point (ICCP) algorithm is widely used in geomagnetic navigation. In order to enhance the anti-interference performance of the ICCP, an improved algorithm is proposed. First, the principle of delta modulation is introduced to generate a geomagnetic matching sequence according to the magnetic fluctuations, this assists finding the optimal quantitative step and matching length; thus, the algorithm's accuracy and real-time performance are improved. Second, in order to solve the problem of geomagnetic matching under an interference environment, a Probability Data Association (PDA) algorithm based on regenerated measurements is adopted. The ideal magnetic value is regarded as a target, and the measured values within the confidence region are taken as the effective measurements of the target. Each of them will give an estimation of the vehicle's position. Considering the constraints of a vehicle's kinematic performance, its final position can be obtained by fusing all effective estimations with the PDA algorithm. Simulation and semi-physical experiments have verified the feasibility and effectiveness of the proposed algorithm. The Regenerated Measurements (RM)-PDA algorithm shows better performance and can be used in practical applications.

Keywords

Geomagnetic-aided navigationGeomagnetic anomalyICCP algorithmDelta modulationProbability data association

Information

Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 1 , January 2020 , pp. 56 - 74
Copyright
Copyright © The Royal Institute of Navigation 2019 

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References

REFERENCES

Al-Labadi, L. and Zarepour, M. (2017). Two-sample Kolmogorov-Smirnov test using a Bayesian non-parametric approach. Mathematical Methods of Statistics, 26(3), 212–225.Google Scholar
Ali, A., Siddharth, S., Syed, Z. and El-Sheimy, N. (2012). Swarm optimization-based magnetometer calibration for personal handheld devices. Sensors, 12(5), 12455–12472.Google Scholar
Alonso, R. and Shuster, M.D. (2002). Twostep: a fast robust algorithm for attitude-independent magnetometer-bias determination. Journal of the Astronautical Sciences, 50(4), 433–451.Google Scholar
Ammann, N., Derksen, A. and Heck, C. (2015). A novel magnetometer-accelerometer calibration based on a least squares approach. International Conference on Unmanned Aircraft Systems, Denver, USA.Google Scholar
Basterretxeairibar, I., Sotés, I. and Uriarte, J.I. (2016). Towards an improvement of magnetic compass accuracy and adjustment. The Journal of Navigation, 69(6), 1325–1340.Google Scholar
Cai, Q., Yang, G., Song, N., Yin, H. and Liu, Y. (2016). Analysis and calibration of the gyro bias caused by geomagnetic field in a dual-axis rotational inertial navigation system. Measurement Science and Technology, 27(6), 105001.Google Scholar
Chulliat, A., Macmillan, S., Alken, P., Beggan, C., Nair,M., Hamilton, B., Woods, A., Ridley, V., Maus, S. and Thomson, A. (2015). The US/UK world magnetic model for 2015–2020. Technical report: National Geophysical Data Center, NOAA.Google Scholar
Crassidis, J.L., Lai, K.L. and Harman, R.R. (2012). Real-time attitude-independent three-axis magnetometer calibration. Journal of Guidance Control & Dynamics, 28(1), 115–120.Google Scholar
Guo, C. and Cai, H. (2013). Feature extraction and geomagnetic matching. The Journal of Navigation, 66(6), 799–811.Google Scholar
Gleason, D.M. (2015). Passive airborne navigation and terrain avoidance using gravity gradiometry. Journal of Guidance Control and Dynamics,18(6), 1450–1458.Google Scholar
Ge, Z., Liu, S. and Li, G. (2017). Error model of geomagnetic-field measurement and extended Kalman-filter based compensation method. Plos One, 12(4), e0173962.Google Scholar
Gulati, D., Zhang, F. and Malovetz, D. (2017). Robust cooperative localization in a dynamic environment using factor graphs and probability data association filter. International Conference on Information Fusion, Xi'an, China.Google Scholar
Heller, W.G. and Jordan, S.K. (2015). Error analysis of two new gradiometer-aided inertial navigation systems. Journal of Spacecraft and Rockets, 13(6), 340.Google Scholar
Huynh, P., Do, T.H. and Yoo, M. (2017). A probability-based algorithm using image sensors to track the LED in a vehicle visible light communication system. Sensors, 17(2), 347.Google Scholar
Inglada, J. and Giros, A. (2004). On the possibility of automatic multi-sensor image registration. IEEE Transactions on Geoscience & Remote Sensing, 42(6): 2104–2120.Google Scholar
Karim, B., David, F. and Antoine, F. (2011). Three-dimensional controlled motion of a microrobot using magnetic gradients. Advanced Robotics, 25(4), 1069–1083.Google Scholar
Luo, S., Wang, Y. and Liu, Y. (2008). Research on geomagnetic-matching technology based on improved ICP algorithm. International Conference on Information and Automation, Changsha, China.Google Scholar
Liang, Y. (2010). Research on technologies of INS/ geomagnetic matching integrated navigation system. PhD Thesis of Harbin Engineering University. (in Chinese)Google Scholar
Liu, Z., Zhang, Q., Pan, M. and Shan, Q. (2016). Compensation of Geomagnetic Vector Measurement System with Differential Magnetic Field Method, IEEE Sensors Journal, 16(24), 9006–9013.Google Scholar
Maus, S. (2010). An ellipsoidal harmonic representation of earth's lithospheric magnetic field to degree and order 720. Geochemistry Geophysics Geosystems, 11, Q06015.Google Scholar
Nyatega, C.O. and Li, S.X. (2015). Study on geomagnetic-matching technology based on ICP algorithm. International Journal of Science & Research, 4(4), 3258–3261.Google Scholar
Oliver, M.A. and Webster, R. (1990). Kriging: a method of interpolation for geographical information systems. International Journal of Geographical Information Systems, 4(3), 313–332.Google Scholar
Qiao, Y.K., Zhang, J.S. and Sun, Y. (2011). A method for geomagnetic matching navigation based on forced de-noising and multi-scale fusion. Journal of Astronautics, 32(1), 53–59. (in Chinese).Google Scholar
Rajalkshmi, D. and Dinakaran, K. (2017). A novel time series pattern matching model combined with ant colony optimization and optimal binary search trees-based segmentation approach. Journal of Computational & Theoretical Nanoscience, 14(7), 5203–5208.Google Scholar
Schindler, H. R. (2009). Delta modulation. IEEE Spectrum, 7(6), 69–78.Google Scholar
Shi, G.G., Zhou, J. and Ge, Z.L. (2010). Navigation error of inertial/geomagnetic navigation technology for cruise aircraft. Journal of Chinese Inertial Technology, 31(4), 427–432.Google Scholar
Teixeira, F.C. and Pascoal, A.M. (2008). Geophysical navigation of autonomous underwater vehicles using geomagnetic information. The 2nd IFAC Workshop on Navigation, Guidance and Control of Underwater Vehicles, Killaloe, Ireland.Google Scholar
Wu, Z., Hu, X. and Wu, M. (2013). An experimental evaluation of autonomous underwater vehicle localization on geomagnetic map. Applied Physics Letters, 103(6), 9–18.Google Scholar
Xiao, J., Duan, X.S. and Qi, X.H. (2017). An adaptive ΔM-ICCP geomagnetic matching algorithm. The Journal of Navigation, 71(03), 1–15.Google Scholar
Xiong, L., Xiao, L. W., Dan, B. B. and Jie, M. (2013). Full tensor gravity gradient aided navigation based on nearest matching neural network. IEEE Conference on Cross Strait Quad-regional Radio Science and Wireless Technology, Chengdu, China.Google Scholar
Zhang, K., Li, Y. and Rizos, J.Z.C. (2014). A study of underwater terrain navigation based on the robust matching method. The Journal of Navigation, 67(4), 569–578.Google Scholar
Zhou, J., Liu, Y. and Ge, Z. (2010). Geomagnetic matching algorithm based on probabilistic neural network. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 1(G1),1–7.Google Scholar