Optimal visual simulation of the self-tracking combustion of the infrared decoy based on the particle system

The high-efficiency simulation test of military weapons has a very important effect on the high cost of the actual combat test and the very demanding operational efficiency. Especially among the simulative emulation methods of the explosive smoke, the simulation method based on the particle system has generated much attention. In order to further improve the traditional simulative emulation degree of the movement process of the infrared decoy during the real combustion cycle, this paper, adopting the virtual simulation platform of OpenGL and Vega Prime and according to their own radiation characteristics and the aerodynamic characteristics of the infrared decoy, has simulated the dynamic fuzzy characteristics of the infrared decoy during the real combustion cycle by using particle system based on the double depth peeling algorithm and has solved key issues such as the interface, coordinate conversion and the retention and recovery of the Vega Prime’s status. The simulation experiment has basically reached the expected improvement purpose, effectively improved the simulation fidelity and provided theoretical support for improving the performance of the infrared decoy.

[1]  Steve Mann Intelligent Image Processing , 2001 .

[2]  Hans-Peter Seidel,et al.  3D-modeling by ortho-image generation from image sequences , 2008, ACM Trans. Graph..

[3]  Masa Inakage,et al.  Non-Linear Perspective Projections , 1991, Modeling in Computer Graphics.

[4]  Doug L. James,et al.  Wavelet turbulence for fluid simulation , 2008, SIGGRAPH 2008.

[5]  Liu Yaozhou Special Effect Building for Missile Fly Track and Fog Based on Particle System , 2004 .

[6]  Ronald Fedkiw,et al.  Wrinkled flames and cellular patterns , 2007, SIGGRAPH 2007.

[7]  Ronald Fedkiw,et al.  Visual simulation of smoke , 2001, SIGGRAPH.

[8]  Steven G. Parker,et al.  Visualizing Particle-Based Simulation Datasets on the Desktop , 2006 .

[9]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[10]  Zhang Qin Study of Particle System Based Flame Modeling and Realization , 2001 .

[11]  Richard Szeliski,et al.  Surface modeling with oriented particle systems , 1992, SIGGRAPH.

[12]  Can Ozan Tan,et al.  Fractal properties of human heart period variability: physiological and methodological implications , 2009, Journal of Physiology.

[13]  Markus H. Gross,et al.  Wavelet turbulence for fluid simulation , 2008, ACM Trans. Graph..

[14]  Niniane Wang,et al.  Rendering falling rain and snow , 2004, SIGGRAPH '04.

[15]  Robert Bridson,et al.  Evolving sub-grid turbulence for smoke animation , 2008, SCA '08.

[16]  Ken Perlin,et al.  Live paint: painting with procedural multiscale textures , 1995, SIGGRAPH.

[17]  Ricki Blau,et al.  Approximate and probabilistic algorithms for shading and rendering structured particle systems , 1985, SIGGRAPH.

[18]  Zhang Jin-sheng Real-time simulation of rain and snow based on particle system and Vega software , 2008 .

[19]  Christopher Horvath,et al.  Directable, high-resolution simulation of fire on the GPU , 2009, SIGGRAPH '09.

[20]  Bernd Hamann,et al.  Real-time procedural volumetric fire , 2007, SI3D.

[21]  Ronald Fedkiw,et al.  Melting and burning solids into liquids and gases , 2006, IEEE Transactions on Visualization and Computer Graphics.

[22]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .