Minimum dispersion coefficient criteria based positioning algorithm for BDS

• Autorzy: Wang L. , Li L.

Identyfikatory

DOI

10.24425/aee.2018.124737

• Języki publikacji: EN

Abstrakty

EN

The BeiDou navigation satellite system (BDS) is one of the four global navigation satellite systems. More attention has been paid to the positioning algorithm of the BDS. Based on the study on the Kalman filter (KF) algorithm, this paper proposed a novel algorithm for the BDS, named as the minimum dispersion coefficient criteria Kalman filter (MDCCKF) positioning algorithm. The MDCCKF algorithm adopts minimum dispersion coefficient criteria (MDCC) to remove the influence of noise with an alpha-stable distribution (ASD) model which can describe non-Gaussian noise effectively, especially for the pulse noise in positioning. By minimizing the dispersion coefficient of the positioning error, the MDCCKF assures positioning accuracy under both Gaussian and non-Gaussian environment. Compared with the original KF algorithm, it is shown that the MDCCKF algorithm has higher positioning accuracy and robustness. The MDCCKF algorithm provides insightful results for potential future research.

Słowa kluczowe

EN

alpha-stable distribution; BeiDou satellites; Kalman filter; minimum dispersion coefficient criteria; non-Gaussian noise; positioning algorithm

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2018
• Tom: Vol. 67, nr 4
• Strony: 739--753
• Opis fizyczny: Bibliogr. 30 poz., rys., wz.

Twórcy

autor

Wang L.

• School of Computer and Communication Engineering University of Science and Technology Beijing (USTB) 100083, Beijing, P. R. China

autor

Li L.

• School of Computer and Communication Engineering University of Science and Technology Beijing (USTB) 100083, Beijing, P. R. China

Bibliografia

• [1] Zhongming Z., Linong L., Xiaona Y., Recent progresses on Beidou/COMPASS and other Global Navigation Satellite Systems (GNSS)-1, Advances in Space Research, vol. 51, no. 6 (2013).
• [2] Zhou Y., Chen X., Mao X., A double-frequency combined positioning algorithm of BeiDou navigation satellite system, 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, China, pp. 9–14 (2017).
• [3] Montenbruck O., Hauschild A., Steigenberger P., Initial assessment of the COMPASS/BeiDou-2 regional navigation satellite system, GPS solutions, vol. 17, no. 2, pp. 211–222 (2013).
• [4] Jiang W., Xi R., Chen H., Accuracy analysis of continuous deformation monitoring using BeiDou navigation satellite system at middle and high latitudes in China, Advances in Space Research, vol. 59, no. 3, pp. 843–857 (2017).
• [5] Li D., He G., Wu C., Algorithm for autonomous navigation of mobile robot measurements based on Beidou/laser radar, 2017 2nd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), IEEE„ Wuhan, China, pp. 305–309 (2017).
• [6] Han Z., Liu J., Li R., A modified differential coherent bit synchronization algorithm for BeiDou weak signals with large frequency deviation, Sensors, vol. 17, no. 7, p. 1568 (2017).
• [7] Wang X., Du J., Li W., An improved high-sensitivity acquisition algorithm for BDS B2 signal, China Satellite Navigation Conference, Shanghai, China, pp. 57–68 (2017).
• [8] Dubovik O., Herman M., Holdak A., Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations, Atmospheric Measurement Techniques, vol. 4, no. 5, p. 975 (2011).
• [9] Guo J., Zhang X., Algorithm of GNSS positioning based on Doppler shift in incomplete condition of insufficient available satellites, AOPC 2017: Space Optics and Earth Imaging and Space Navigation. International Society for Optics and Photonics, Beijing, China, p. 104630F (2017).
• [10] Huang J., Tan H. S., A low-order DGPS-based vehicle positioning system under urban environment, IEEE/ASME Transactions on mechatronics, vol. 11, no. 5, pp. 567–575 (2006).
• [11] Soubielle J., Fijalkow I., Duvaut P., GPS positioning in a multipath environment, IEEE Transactions on Signal Processing, vol. 50, no. 1, pp. 141–150 (2002).
• [12] Viandier N., Nahimana D. F., Marais J., GNSS performance enhancement in urban environment based on pseudo-range error model, Position, Location andNavigation Symposium, California,United States, pp. 377–382 (2008).
• [13] He F., Zhou S. S., Hu X.G., Satellite-station time synchronization information based real-time orbit error monitoring and correction of navigation satellite in Beidou System, Science China Physics, Mechanics & Astronomy, vol. 57, no. 7, pp. 1395–1403 (2014).
• [14] Li M., Qu L., Zhao Q., Precise point positioning with the BeiDou navigation satellite system, Sensors, vol. 14, no. 1, pp. 927–943 (2014).
• [15] Takeuchi I., Bengio Y., Kanamori T., Robust regression with asymmetric heavy-tail noise distributions, Neural Computation, vol. 14, no. 10, pp. 2469–2496 (2002).
• [16] Zechner C., Seelig G., Rullan M., Molecular circuits for dynamic noise filtering, Proceedings of the National Academy of Sciences of the United States of America, vol. 113, no. 17, pp. 4729–4734 (2016).
• [17] Choi H. D., Ahn C. K., Lim M. T., Time-domain filtering for estimation of linear systems with colored noises using recent finite measurements, Measurement, vol. 46, no. 8, pp. 2792–2797 (2013).
• [18] Plataniotis K. N., Androutsos D., Venetsanopoulos A.N., Nonlinear filtering of non-Gaussian noise, Journal of Intelligent and Robotic Systems, vol. 19, no. 2, pp. 207–231 (1997).
• [19] Nolan J. P., Fitting data and assessing goodness-of-fit with stable distributions, PhD Thesis, Department of Mathematics and Statistics, American University, Washington DC (1999).
• [20] Shao M., Nikias C. L., Signal processing with fractional lower order moments: stable processes and their applications, Proceedings of the IEEE, vol. 81, no. 7, pp. 986–1010 (1993).
• [21] Magill D., Optimal adaptive estimation of sampled stochastic processes, IEEE Transactions on Automatic Control, vol. 10, no. 4, pp. 434–439 (1965).
• [22] Izanloo R., Fakoorian S. A., Yazdi H. S., Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise, 2016 Annual Conference on Information Science and Systems (CISS), IEEE, New Jersey, USA, pp. 500–505 (2016).
• [23] Kemp F., An introduction to sequential Monte Carlo methods, Journal of the Royal Statistical Society: Series D (The Statistician), vol. 52, no. 4, pp. 694–695 (2003).
• [24] Li D., Xu L., Li X., Influence of UTC parameters generating method on BDS positioning accuracy, Frequency and Time Forum and IEEE International Frequency Control Symposium (EFTF/IFC), Paderborn, Germany, pp. 699–702 (2017).
• [25] Kang C., A differential dynamic positioning algorithm based on GPS/Beidou, Procedia engineering, vol. 137, pp. 590–598 (2016).
• [26] Julier S. J., Uhlmann J. K., Unscented filtering and nonlinear estimation, Proceedings of the IEEE, vol. 92, no. 3, pp. 401–422 (2004).
• [27] Chen B., Liu X., Zhao H., Maximum correntropy Kalman filter, Automatica, vol. 76, pp. 70–77 (2017).
• [28] Liu W., Zhang H., Wang Z., State estimation for discrete-time Markov jump linear systems based on orthogonal projective theorem, International Journal of Control, Automation and Systems, vol. 10, no. 5, pp. 1049–1054 (2012).
• [29] Lewis F. L., Optimal estimation: with an introduction to stochastic control theory, CRC Press (1986).
• [30] Peng S., Zhao D., Huang Z., Research and implementation of BDS/GPS coarse time navigation algorithm, China Satellite Navigation Conference (CSNC), Changsha, China, pp. 227–240 (2016).

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).