Abstract
In many fields of geodesy applications, state and parameter estimation are of major importance within modeling of on-line processes. The fundamental block of such processes is a filter for recursive estimation. Kalman Filter is the well known filter, a simple and efficient algorithm, as an optimal recursive Bayesian estimator for a somewhat restricted class of linear Gaussian problems. However, in the case that state and/or measurement functions are highly non-linear and the density function of process and/or measurement noise are non-Gaussian, classical filters do not yield satisfying estimates. So it is necessary to adopt alternative filtering techniques in order to provide almost optimal results. A number of such filtering techniques will be reviewed in this contribution, but the main focus lays on the sequential Monte Carlo (SMC) estimation. The SMC filter (well known as particle filter) allows to reach this goal numerically, and works properly with nonlinear, non-Gaussian state estimation. The main idea behind the SMC filter is to approximate the posterior PDF by a set of random particles, which can be generated from a known PDF. These particles are propagated through the nonlinear dynamic model. They are then weighted according to the likelihood of the observations. By means of the particles the true mean and the covariance of the state vector are estimated. However, the computational cost of particle filters has often been considered as their main disadvantage. This occur due to the large, sufficient number of particles to be drawn. Therefore a more efficient approach will be presented, which is based on the combination of SMC filter and the Kalman Filter. The efficiency of the developed filters will be demonstrated through application to a method for direct georeferencing tasks for a multi-sensor system (MSS).
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References
Alkhatib H, Paffenholz J-A, Kutterer H (2012) Sequential Monte Carlo Filtering for nonlinear GNSS trajectories. In: Nico S (ed) VII Hotine-Marussi Symposium on mathematical geodesy. Proceedings of the Symposium in Rome, 6–10 June, 2009, pp.81–86. Springer (International Association of Geodesy Symposia, 137), Berlin/New York
Aussems T (1999) Positionsschätzung von Landfahrzeugen mittels KALMAN-Filterung aus Satelliten- und Koppelnavigationsbeobachtungen. Veröffentlichungen des Geodätischen Instituts der Rheinisch-Westfälischen Technischen Hochschule Aachen, Nr. 55, Aachen
Bar-Shalom Y, Li XR, Kirubarajan T, Li X-R (2001) Estimation with applications to tracking and navigation. Theory algorthims and software. Wiley, New York
Doucet A, Freitas N, Gordon N (2001) Sequential Monte Carlo methods in practice. Springer, New York
Eichhorn A (2008) Analysis of dynamic deformation processes with adaptive Kalman-filtering. J Appl Geodesy 1(1):9–15
Gelb A (1974) Applied optimal estimation. MIT, Cambridge
Julier SJ, Uhlmann JK (1997) A new extension of the Kalman filter to nonlinear systems. In: SPIE Proceedings of AeroSense. The 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls. SPIE, Orlando, FL, USA
Paffenholz J-A, Alkhatib H, Kutterer H (2010) Direct georeferencing of a static terrestrial laser scanner. J Appl Geodesy 4(3):115–126
Ristic B, Arulampalam S, Gordon N (2004) Beyond the Kalman filter. Particle filters for tracking applications. Artech House, Boston
Särkkä S (2006) Recursive Bayesian inference on stochastic differential equations. Ph.D. thesis, Helsinki University of Technology
Simon D (2006) Optimal state estimation. Kalman, H infinity, and nonlinear approaches // Kalman, H [infinity] and nonlinear approaches. Wiley, Hoboken
Sternberg H (2000) Zur Bestimmung der Trajektorie von Landfahrzeugen mit einem hybriden Messsystem. Schriftenreihe des Studienganges Geodäsie und Geoinformation, Universität der Bundeswehr Mänchen, No. 67, Neubiberg
Storvic G (2002) Particle filters in state space models with the presence of unknown static parameters. IEEE Trans Signal Process 90(2):281–289
Yang X, Xing K, Shi K, Pan Q (2008) Joint parameter and state estimation in particle filtering and stochastic optimization. J Control Theory Appl 6(2):215–220
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Alkhatib, H. (2015). Alternative Nonlinear Filtering Techniques in Geodesy for Dual State and Adaptive Parameter Estimation. In: Kutterer, H., Seitz, F., Alkhatib, H., Schmidt, M. (eds) The 1st International Workshop on the Quality of Geodetic Observation and Monitoring Systems (QuGOMS'11). International Association of Geodesy Symposia, vol 140. Springer, Cham. https://doi.org/10.1007/978-3-319-10828-5_16
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DOI: https://doi.org/10.1007/978-3-319-10828-5_16
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