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|Title:||Kalman filtering for navigation application||Authors:||Zhou, JingJing.||Keywords:||DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering||Issue Date:||2011||Abstract:||In 1960, R.E. Kalman published his papers on a recursive predictive filter that is based on the use of state space techniques and recursive algorithm. Since then, the Kalman filter has been the subject of extensive research and application, particularly in the field of navigation. Nowadays, most of the navigation systems use not only the Global Positioning System (GPS) but also an Inertial Navigation System (INS) to help driver to find his way. These two systems complement each other and improve the navigation accuracy and reliability. And the Kalman filter provides the basis for this application. In this report, the task is to program an indirect Kalman filter in Matlab to estimate the error states of the INS and correct the navigation states with GPS measurements to prevent divergence due to modeling errors. The study of Kalman filtering includes a description of the standard Kalman filter and its algorithm with 2 main steps: the prediction and correction steps. Interesting examples, such as applying the Kalman filter to estimate the Cumulative Grade Point Average (CGPA) were explored to provide an understanding with its practical aspects. The elementary study of INS is based on Matlab program of simINS.m, which contributed by DSO. Progressively, error state equations of INS were established and indirect feedforward Kalman filter was used to estimate the error states, thereby correct the navigation states. The results are verified against results from original INS simulation, which without Kalman filter optimization.||URI:||http://hdl.handle.net/10356/46500||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
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