On robust constrained Kalman filter for dynamic errors-in-variables model

References

• Amiri-Simkooei, A.R., 2018. Weighted total least squares with constraints: alternative derivation without using Lagrange multipliers. Journal of surveying engineering, 144 (2), 06017005. doi: 10.1061/(ASCE)SU.1943-5428.0000253
   Web of Science ®Google Scholar
• Bifulco, I., Raiconi, G., and Scarpa, R., 2009. Computer algebra software for least squares and total least norm inversion of geophysical models. Computers & geosciences, 35 (7), 1427–1438. doi: 10.1016/j.cageo.2008.11.005
   Web of Science ®Google Scholar
• Gelb, A., 1974. Applied optimal estimation. Cambridge: MIT Press.
   Google Scholar
• Gong, X.Q. and Li, Z.L., 2017. A robust weighted total least-squares solution with Lagrange 391 multiplers. Survey review, 49 (354), 176–185. doi: 10.1080/00396265.2016.1150088
   Web of Science ®Google Scholar
• Gong, X. and Li, Z., 2018. Bridge pier settlement prediction in high-speed railway via autoregressive model based on robust weighted total least-squares. Survey review, 50 (359), 147–154. doi: 10.1080/00396265.2016.1236162
   Web of Science ®Google Scholar
• Grafarend, E., and Awange, J., 2012. Applications of linear and nonlinear models: fixed effects, random effects. Berlin: Springer Science & Business Media.
   Google Scholar
• Huber, P.J., 1981. Robust statistics. New York: Wiley.
   Google Scholar
• Jazwinski, A.H., 1970. Stochastic process and filtering theory. New York: Academic Press.
   Google Scholar
• Jin, Y., et al., 2017. Partial total-least-squares adjustment of condition equations with application to a rectangular building adjustment in a GIS. Journal of surveying engineering, 144 (1), 04017021. doi: 10.1061/(ASCE)SU.1943-5428.0000246
   Web of Science ®Google Scholar
• Julier, S.J. and Uhlmann, J.K., 1997. A new extension of the Kalman filter to nonlinear systems. In: Proceedings of the SPIE AeroSense international symposium on aerospace/defense sensing, simulation and controls, Orlando, FL.
   Google Scholar
• Julier, S.J., Uhlmann, J.K., and Durrant-Whyte, H., 1995. A new approach for filtering nonlinear systems. In: Proceedings of the American control conference, 1628–1632. Seattle.
   Google Scholar
• Kalman, R.E., 1960. A new approach to linear filtering and prediction problems, transactions of the ASME, series D. Journal of basic engineering, 82, 34–45.
   Google Scholar
• Krarup, T., Juhl, J., and Kubik, K., 1980. Götterdämmerung over least squares adjustment. Proceedings of 14th congress of the international society of photogrammetry, Vol. B3, 369–378. Hamburg.
   Google Scholar
• Lu, J., et al., 2014. Robust total least squares with reweighting iteration for three-dimensional similarity transformation. Survey review, 46 (334), 28–36. doi: 10.1179/1752270613Y.0000000050
   Web of Science ®Google Scholar
• Mahboub, V., Amiri-Simkooei, A.R., and Sharifi, M.A., 2013a. Iteratively reweighted total least squares: a robust estimation in errors-in-variables models. Survey review, 45 (329), 92–99.
   Web of Science ®Google Scholar
• Mahboub, V. and Mohammadi, D., 2018. A constrained total extended Kalman filter for integrated navigation. The journal of navigation. doi:10.1017/S0373463318000012.
   Web of Science ®Google Scholar
• Mahboub, V., Saadatseresht, M., and Ardalan, A.A., 2017a. A general weighted total Kalman filter algorithm with numerical evaluation. Studia geophysica et geodaetica, 61 (1), 19–34. doi: 10.1007/s11200-016-0815-7
   Web of Science ®Google Scholar
• Mahboub, V., Saadatseresht, M., and Ardalan, A.A., 2017b. A solution to dynamic errors-in-variables within system equations. Acta geodaetica et geophysica, 53 (1), 31–44. doi: 10.1007/s40328-017-0201-0
   Web of Science ®Google Scholar
• Mahboub, V., Saadatseresht, M., and Ardalan, A.A., 2017c. On constrained integrated total Kalman filter for integrated direct geo-referencing. Survey review, 1–9. doi:10.1080/00396265.2017.1341736.
   Web of Science ®Google Scholar
• Mahboub, V. and Sharifi, M.A., 2013b. On weighted total least-squares with linear and quadratic constraints. Journal of geodesy, 87 (3), 279–286. doi: 10.1007/s00190-012-0598-8
   Web of Science ®Google Scholar
• Mahboub, V. and Sharifi, M.A., 2013c. Erratum to: on weighted total least squares with linear and quadratic constraints. Journal of Geodesy, 87, 607–608. doi: 10.1007/s00190-013-0620-9
   Web of Science ®Google Scholar
• Pontius, R.G., Thontteh, O., and Chen, H., 2008. Components of information for multiple resolution comparison between maps that share a real variable. Environmental and ecological statistics, 15 (2), 111–142. doi: 10.1007/s10651-007-0043-y
   Web of Science ®Google Scholar
• Sanso, F., 1986. Statistical methods in physical geodesy. In: H Sunkel, ed. Mathematical and numerical techniques in physical geodesy. Lecture notes in earth sciences , Vol. 7. Berlin: Springer, 49–155.
   Google Scholar
• Schaffrin, B. and Iz, H.B., 2008. Towards total Kalman filtering for mobile mapping. The international archives of the photogrammetry, remote sensing and spatial information sciences, 36 (Part 5/C55), 270–275.
   Google Scholar
• Schaffrin, B., and Uzun, S., 2011. Errors-in variables for mobile mapping algorithms in the presence of outliers. Arch. Photogramm. Cartogr Remote Sens, 22, 377–387.
   Google Scholar
• Sheta, B. 2012. Vision based navigation (VBN) of unmanned aerial vehicles (UAV). Thesis (PhD), Calgary, Alberta.
   Google Scholar
• Teunissen, P., 1989. First and second moments of non-linear least-squares estimators. Journal of geodesy, 63 (3), 253–262.
   Google Scholar
• Wang, B., Li, J.C., and Liu, C., 2016. A robust weighted total least squares algorithm and its geodetic applications. Studia geophysica et geodaetica. doi:10.1007/s11200-015-0916-8.
   Web of Science ®Google Scholar
• Wang, L., Xu, C., and Wen, Y., 2013. Fault parameters of 2008 Qinghai Dacaidan Mw 6.3 earthquake from STLN inversion and InSAR data. Acta geodaetica et cartographica sinica, 42 (2), 168–176.
   Google Scholar
• Wang, L. and Zhao, Y., 2017a. Scaled unscented transformation of nonlinear error propagation: accuracy, sensitivity, and applications. Journal of surveying engineering, 144 (1), 04017022. doi: 10.1061/(ASCE)SU.1943-5428.0000243
   Web of Science ®Google Scholar
• Wang, L. and Zhao, Y., 2017b. Unscented transformation with scaled symmetric sampling strategy for precision estimation of total least squares. Studia geophysica et geodaetica, 61 (3), 385–411. doi: 10.1007/s11200-016-1113-0
   Web of Science ®Google Scholar
• Wang, L. and Zhao, Y., 2018. Second-order approximation function method for precision estimation of total least squares. Journal of surveying engineering, 145 (1), 04018011. doi: 10.1061/(ASCE)SU.1943-5428.0000266
   Web of Science ®Google Scholar
• Willmott, C.J. and Matsuura, K., 2006. On the use of dimensioned measures of error to evaluate the performance of spatial interpolators. International journal of geographical information science, 20 (1), 89–102. doi: 10.1080/13658810500286976
   Web of Science ®Google Scholar
• Yang, Y., 1999. Robust estimation of geodetic datum transformation. Journal of geodesy, 73, 268–274. doi: 10.1007/s001900050243
   Web of Science ®Google Scholar
• Yang, Y., Song, L., and Xu, T., 2002. Robust estimator for correlated observations based on bifactor equivalent weights. Journal of Geodesy, 76, 353–358. doi: 10.1007/s00190-002-0256-7
   Web of Science ®Google Scholar
• Yu, H., et al., 2018. Generalized total Kalman filter algorithm of nonlinear dynamic errors-in-variables model with application on indoor mobile robot positioning. Acta geodaetica et geophysica, 53 (1), 107–123. doi: 10.1007/s40328-017-0207-7
   Web of Science ®Google Scholar