Application of The KNN Algorithm in Determining the Orientation of The Probability Area Containing The Ship Position by GPS Systems on Hai Phong Coastal Area

Thai Duong Nguyen, Trong Duc Nguyen

Abstract


The need for navigation for the maritime industry is also very urgent. Today, most commercial boats have receivers and positioning signal receivers, giving an accuracy of several meters. For vessels arriving at, or passing through small canals, the ability to locate with an accuracy of less than 1 meter is necessary. In these cases, the use of DGPS technology is necessary. The determination of ship location and navigation depends on global satellite navigation systems, mainly GPS systems. In maritime practice, the position of the specified vessel is considered the most probable position and will be the center of the circle of probability area containing the ship position. However, the probability area containing the ship position is scalar, the radius of error of the circle of probability area depends on many factors, such as the deviation of geodetic system, the accuracy of the chart. Therefore, the determination of the most probable position with the highest accuracy is a quite complex problem. The demand for data processing is also greater; Machine Learning is thus contributing to solving this problem. In the framework of the article, the authors propose the application of the KNN algorithm to determine the orientation of the probability area containing the ship position with the most probable positions. The objective of this study is to improve efficiency and safety in maneuvering and navigation for sea vessels and testing for Hai Phong coastal area.

Keywords


KNN algorithm; GPS systems; ship position; maritime industry.

Full Text:

PDF

References


G. Xu and Y. Xu, GPS: theory, algorithms and applications. Springer, 2016.

Y. Huang and Q. Zhang, "Identification of anomaly behavior of ships based on KNN and LOF combination algorithm," in AIP Conference Proceedings, 2019, vol. 2073, no. 1, p. 020090: AIP Publishing.

X. Li, X. Zhang, X. Ren, M. Fritsche, J. Wickert, and H. Schuh, "Precise positioning with current multi-constellation global navigation satellite systems: GPS, GLONASS, Galileo and BeiDou," ScientificReports, vol. 5, p. 8328, 2015.

J. T. Pisz and B. H. Inouye, "GPS gate system," ed: Google Patents, 2016.

K.-H. Yang, X. Wang, S. Liu, and L.-L. Zhao, "Precision analysis on result of GPS pseudo-range point positioning and differential positioning," in Mechatronics And Manufacturing Technologies-Proceedings Of The International Conference (Mmt 2016), 2017, p. 3: World Scientific.

H. Greidanus, M. Alvarez, C. Santamaria, F.-X. Thoorens, N. Kourti, and P. Argentieri, "The SUMO ship detector algorithm for satellite radar images," Remote Sensing, vol. 9, no. 3, p. 246, 2017.

J. Bhatti and T. E. Humphreys, "Hostile control of ships via false GPS signals: Demonstration and detection," NAVIGATION: Journal of the Institute of Navigation, vol. 64, no. 1, pp. 51-66, 2017.

K. Jansen, N. O. Tippenhauer, and C. Pöpper, "Multi-receiver GPS spoofing detection: error models and realization," in Proceedings of the 32nd Annual Conference on Computer Security Applications, 2016, pp. 237-250: ACM.

L. Deng, H. Guo, N. Xiros, and M. Yu, "A research on roll angle calculations based on IMU/GPS compass for ships," in 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS), 2016, pp. 976-980: IEEE.

T. B. Kiland and J. S. Gray, The Military GPS: Cutting Edge Global Positioning System. Enslow Publishing, LLC, 2016.

M. H. Assaf, E. M. Petriu, and V. Groza, "Ship track estimation using GPS data and Kalman Filter," in 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2018, pp. 1-6: IEEE.

B. Jigena et al., "Improving the learning process in the subject of Basic Maritime Training using GPS and Google Earth as useful tools," 2016.

F. Mazzarella, V. F. Arguedas, and M. Vespe, "Knowledge-based vessel position prediction using historical AIS data," in 2015 Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2015, pp. 1-6: IEEE.

Š. Pedišić, "Navigation instruments and equipment," University of Zadar. Maritime Department. Division of Nautical Studies., 2016.

P. Rieth and U. Stählin, "Update of digital maps and position-finding," ed: Google Patents, 2016.

Y. Shi, C. Shen, H. Fang, and H. Li, "Advanced control in marine mechatronic systems: A survey," IEEE/ASME Transactions on Mechatronics, vol. 22, no. 3, pp. 1121-1131, 2017.

J. Xiong, L. Shu, Q. Wang, W. Xu, and C. Zhu, "A scheme on indoor tracking of dynamic ship positioning based on distributed multi-sensor data fusion," IEEE Access, vol. 5, pp. 379-392, 2016.

X. Yang, H. Sun, X. Sun, M. Yan, Z. Guo, and K. Fu, "Position detection and direction prediction for arbitrary-oriented ships via multitasking rotation region convolutional neural network," IEEE Access, vol. 6, pp. 50839-50849, 2018.

S. D. IlÄev, Global mobile satellite communications theory: for maritime, land, and aeronautical applications. Springer, 2016.

P. K. Gaikwad and S. Pawar, "Implementation of real-time GPS receiver system for providing navigation based services and SMS tracking," in 2015 International Conference on Industrial Instrumentation and Control (ICIC), 2015, pp. 630-634: IEEE.

A. L. Duca, C. Bacciu, and A. Marchetti, "A K-nearest neighbor classifier for ship route prediction," in OCEANS 2017-Aberdeen, 2017, pp. 1-6: IEEE.

A. Deep, M. Mittal, and V. Mittal, "Application of Kalman Filter in GPS Position Estimation," in 2018 IEEE 8th Power India International Conference (PIICON), 2019, pp. 1-5: IEEE.




DOI: http://dx.doi.org/10.18517/ijaseit.9.3.8869

Refbacks

  • There are currently no refbacks.



Published by INSIGHT - Indonesian Society for Knowledge and Human Development