Adaptive median filter for geographic coordinates of the satellite navigation system receiver
Authors: Veltishchev V.V., Romashko A S. | Published: 30.04.2025 |
Published in issue: #5(782)/2025 | |
Category: Mechanical Engineering and Machine Science | Chapter: Robots, Mechatronics and Robotic Systems | |
Keywords: splash filtering, geographic coordinates, median filter, adaptive filter, satellite navigation system |
Receiver of the underwater vehicle satellite navigation system is a source of geographic coordinates; accuracy in their determination affects the accuracy of the system. Errors in the receiver data of the satellite navigation system appear in the form of noise and aperiodic significant deviations from the real value of short duration, i.e. splashes. The paper presents a developed adaptive median filter algorithm that allows filtering out noise and splashes in the geographic coordinates of the satellite navigation system receiver. The proposed filter is operational in all modes of the underwater vehicle motion, introduces no delays, and does not require the use of data from the additional measurement devices. The filter parameters are set based on dynamic characteristics of the underwater vehicle, and are not associated with characteristics of a specific receiver of the satellite navigation system.
EDN: HXOTCN, https://elibrary/hxotcn
References
[1] Kanghui H., Chaoyang D. A fuzzy strong tracking extended Kalman filter for UAV navigation considering interruption of GPS signal. IEEE ICPICS, 2019, pp. 254–259, doi: https://doi.org/10.1109/ICPICS47731.2019.8942402
[2] Katwe S., Lyer N., Khan M. et al. Particle filter based localization of autonomous vehicle. 2nd GCAT, 2021, doi: https://doi.org/10.1109/GCAT52182.2021.9587461
[3] Liang W., Li K. Anti-spoofing Kalman filter for GPS/rotational INS integration. Measurement, 2022, vol. 193, art. 110962, doi: https://doi.org/10.1016/j.measurement.2022.110962
[4] Chunhakam P., Pummarin P., Jeen-im P. et al. GPS positon predicting system by Kalman filter with velocity from OBD and direction from magnetometer. 9th iEECON, 2021, pp. 444–447, doi: https://doi.org/10.1109/iEECON51072.2021.9440239
[5] Cahyadi M.N., Asfihani T., Mardiyanto R. et al. Performance of GPS and IMU sensor fusion using unscented Kalman filter for precise i-Boat navigation in infinite wide waters. Geod. Geodyn., 2023, vol. 14, no. 3, pp. 265–274, doi: https://doi.org/10.1016/j.geog.2022.11.005
[6] Rekhman A., Shakhid Kh., Afzal M.A. et al. Accurate and direct GNSS/PDR integration using extended Kalman filter for pedestrian smartphone navigation. Giroskopiya i navigatsiya, 2020, vol. 28, no. 2, pp. 91–108, doi: https://doi.org/10.17285/0869-7035.0034 (in Russ.). (Eng. version: Gyroscopy Navig., 2020, vol. 11, no. 2, pp. 124–137, doi: https://doi.org/10.1134/S2075108720020054)
[7] Liu Z., Liu J., Xu X. et al. DeepGPS: deep learning enhanced GPS positioning in urban canyons. IEEE Trans. Mob. Comput., 2024, vol. 23, no. 1, pp. 376–392, doi: https://doi.org/10.1109/TMC.2022.3208240
[8] Jiang X., Li Q., Wei D. A BDS/GPS integrated positioning algorithm based on Kalman filter and deep neural networks. 16th CISP-BMEI, 2023, doi: https://doi.org/10.1109/CISP-BMEI60920.2023.10373356
[9] Zhao Sh., Lukyanov V.V. [Application of neural network in sins/gnss integrated navigation system]. XLVII Akademicheskie chteniya po kosmonavtike. T. 3 [XLVII Academic Readings. Vol. 3]. Moscow, Bauman MSTU Publ., 2023, pp. 189–191. (In Russ.).
[10] Yan S., Wu D., Wang W. et al. An analysis of method and performance for GNSS/SINS integrated navigation assisted by recurrent neural network. Journal of Air Force Engineering University, 2021, vol. 22, no. 5, pp. 61–66.
[11] Kumar N.A., Rao G.S., Arasavali N. Development of advanced extended Kalman filter for precise estimation of GPS receiver position. WiSPNET, 2019, pp. 213–216, doi: https://doi.org/10.1109/WiSPNET45539.2019.9032769
[12] Mosavi M.R., Tabatabaei A., Zandi M.J. Positioning improvement by combining GPS and GLONASS based on Kalman filter and its application in GPS spoofing situations. Gyroscopy Navig., 2016, vol. 7, no. 4, pp. 318–325, doi: https://doi.org/10.1134/S2075108716040088
[13] Shokri S., Rahemi N., Mosavi M.R. Improving GPS positioning accuracy using weighted Kalman filter and variance estimation methods. CEAS Aeronaut. J., 2020, vol. 11, no. 2, pp. 515–527, doi: https://doi.org/10.1007/s13272-019-00433-x
[14] Ma J. BDS/GPS deformation analysis of a long-span cable-stayed bridge based on colored noise filtering. Geod. Geodyn., 2023, vol. 14, no. 2, pp. 163–171, doi: https://doi.org/10.1016/j.geog.2022.08.005
[15] Qu X., Ding X., Xu Y.L. et al. Real-time outlier detection in integrated GNSS and accelerometer structural health monitoring systems based on a robust multi-rate Kalman filter. J. Geod., 2023, vol. 97, art. 38, https://doi.org/10.1007/s00190-023-01724
[16] Zhou W., Ding K., Liu P. et al. Spatiotemporal filtering for continuous GPS coordinate time series in mainland China by using independent component analysis. Remote Sens., 2022, vol. 14, no. 12, art. 2904, doi: https://doi.org/10.3390/rs14122904
[17] Li L., Kuhlmann H. Real-time deformation measurements using time series of GPS coordinates processed by Kalman filter with shaping filter. Survey Review, 2012, vol. 44, no. 326, pp. 189–197, doi: https://doi.org/10.11791/1752270611Y.0000000022
[18] Wang J., Ding K., Sun H. et al. Noise reduction and periodic signal extraction for GNSS height data in the study of vertical deformation. Geod. Geodyn., 2023, vol. 14, no. 6, pp. 573–581, doi: https://doi.org/10.1016/j.geog.2023.07.002
[19] Yoonsong H., Kim H.G. A real-time filtering method of positioning data with moving window mechanism. Computer Engineering and Intelligent Systems, 2012, vol. 3, no. 7, pp. 20–31.
[20] Fyalkovskiy A.L. Data processing in the geodetic monitoring of dynamic structures using GNSS. Inzhenernye izyskaniya [Engineering Survey], 2017, vol. 11, no. 9, pp. 42–52, doi: https://doi.org/10.25296/1997-8650-2017-9-42-52 (in Russ.).