Using TAN(Terrain Aided Navigation) technology for remote, precise autonomous positioning and navigation has become an important research topic in aircraft navigation area. With the expansion of the aircraft space range and the improving task requirement, there are increasing demands for the performance of their autonomous navigation system. Therefore, it has been very useful and vital to study the high performance TAN algorithms. Based on the application background of the UAVs' TAN system, a concrete research on the TAN algorithm using GSPF (Gaussian sum particle filter) has been carried out and the simulation experiment environment has been built up in order to verify the reliability and effectiveness of the method. The main work is summarized as follows: 1. The principle of TAN and its application area are introduced. By reviewing the recent TAN methods, the main drawbacks are pointed out. Since the TAN problem is essentially a matter of nonlinear non-gaussian filter estimation, there are more advantages to adopt the gaussian sum particle filter method which is coincidence with such filtering problem model. What's more, it brings out higher computational efficiency. 2. The modeling and simulation of the data demanded by the TAN system are carried out. First of all, the terrain map is derived through the discussion of the mathematics model of terrain and the use of random digital terrain generation technology. Then, through the simulation of the difference between the real terrain and DEM data, the error model of the DEM (digital elevation model) is analyzed. Finally, the sequences of the altimeter data are obtained using the nearest point radar altimeter model. 3. A TAN approach based on Gaussian sum particle filter is proposed. Based on the GSPF theory, we build a new TAN dynamic system model and GSPF method for TAN system. Meanwhile, the simulation experiments are carried out in areas with different features. Meanwhile, the simulation experiments are carried out in areas with different features. What is more, compared with the typical methods which based on nonlinear filtering technology, the new method shows obvious advantages in match precision and adaptability.
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