Three-dimensional scene reconstruction using optimal information fusion

1991 ◽  
Author(s):  
Lang Hong
Keyword(s):  
Information Fusion ◽  
Three Dimensional ◽  
Scene Reconstruction ◽  
Optimal Information

scholarly journals Pursuer Navigation Based on Proportional Navigation and Optimal Information Fusion

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shulin Feng ◽  
Zhanxin Li ◽  
Li Liu ◽  
Hongyong Yang ◽  
Yuanhua Yang ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Information Fusion ◽  
Three Dimensional ◽  
Proportional Navigation ◽  
Kinematics Model ◽  
Application Range ◽  
Environment Simulation ◽  
Optimal Information ◽  
Point To Point ◽  
Proportional Navigation Law

Pursuer navigation is proposed based on the three-dimensional proportional navigation law, and this method presents a family of navigation laws resulting in a rich behavior for different parameters. Firstly, the kinematics model for the pursuer and the target is established. Secondly, the proportional navigation law is deduced through the kinematics model. Based on point-to-point navigation, obstacle avoidance is implemented by adjusting the control parameters, and the combination can enrich the application range of obstacle avoidance and guidance laws. Thirdly, information fusion weighted by diagonal matrices is used for decreasing the tracking precision. Finally, simulations are conducted in the MATLAB environment. Simulation results verify the availability of the proposed navigation law.

Three-dimensional target tracking strategy based on guidance laws and optimal information fusion

2020 ◽  
pp. 014233122096279
Author(s):  
Yihua Dong ◽  
Shulin Feng ◽  
Liuchen Tai
Keyword(s):  
Information Fusion ◽  
Control Strategy ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Proportional Navigation ◽  
Application Range ◽  
Optimal Information ◽  
Guidance Laws ◽  
Tracking Strategy ◽  
Three Dimensional Space

This paper presents a method for pursuer to track a moving target in a three-dimensional space. The method is based on the guidance laws combined with the kinematics equations of the pursuer and the target. The maneuvers of the target are prior unknown to the pursuer. Guidance laws used for tracking are the deviated pursuit and the proportional navigation, and the method presents a family of navigation laws resulting in a rich behavior for different parameters. For the three-dimensional scenario, two cases-not presenting interference and presenting interference are considered. In the absence of interference, the control strategy is proposed to implement the problem of tracking. In the presence of interference, an optimal information fusion Kalman filter weighted by scalars and guidance laws are combined to improve the trajectory tracking precision, and the combination can enrich the application range of information fusion and guidance laws. Simulations are conducted to demonstrate the effectiveness and reliability of the proposed control strategy.

An Information Fusion Algorithm Based on Kalman Filtering

2013 ◽  
Vol 444-445 ◽  
pp. 1072-1076
Author(s):  
Xiu Hu Tan
Keyword(s):  
Mathematical Model ◽  
Information Fusion ◽  
Minimum Mean Square Error ◽  
Minimum Variance ◽  
Probability Method ◽  
Fusion Algorithm ◽  
Multisensor Systems ◽  
Optimal Information ◽  
Noise Statistics ◽  
The Mathematical Model

For the multisensor systems with unknown noise variances, by the statistics method, the mathematical model and the noise statistics are essential, and this limitation was settled by adaptive algorithm. The adaptive Kalman filter was proposed to solve the filtering problem of the system with unknown mathematical model or noise statistics in information fusion. Based on the probability method and the scalar weighting optimal information fusion criterion in the minimum variance sense, the algorithm can not only optimize the multi-channel data, but also obtain the minimum mean square error (MMSE) by introducing fusion equation, namely the algorithm is optimal under the sense of MMSE, and the error is the least than the original Kalman information fusion algorithm. The test result shows that the algorithm can precede information fusion effectively under the distributed acquisition system.

Three-Dimensional Scene Reconstruction

2011 ◽  
pp. 142-162
Author(s):  
Dimitrios Chrysostomou ◽  
Antonios Gasteratos
Keyword(s):  
Vision System ◽  
Three Dimensional ◽  
3D Models ◽  
Scene Reconstruction ◽  
Depth Information ◽  
Vision Systems ◽  
Working Space ◽  
Scene Structure ◽  
Long Time ◽  
Dimensional Surface

The production of 3D models has been a popular research topic already for a long time, and important progress has been made since the early days. During the last decades, vision systems have established to become the standard and one of the most efficient sensorial assets in industrial and everyday applications. Due to the fact that vision provides several vital attributes, many applications tend to use novel vision systems into domestic, working, industrial, and any other environments. To achieve such goals, a vision system should robustly and effectively reconstruct the 3D surface and the working space. This chapter discusses different methods for capturing the three-dimensional surface of a scene. Geometric approaches to three-dimensional scene reconstruction are generally based on the knowledge of the scene structure from the camera’s internal and external parameters. Another class of methods encompasses the photometric approaches, which evaluate the pixels’ intensity to understand the three-dimensional scene structure. The third and final category of approaches, the so-called real aperture approaches, includes methods that use the physical properties of the visual sensors for image acquisition in order to reproduce the depth information of a scene.

Color holograms synthesis framework for three-dimensional scene reconstruction

2015 ◽  
Author(s):  
Pasquale Memmolo ◽  
Marco Leo ◽  
Cosimo Distante ◽  
Melania Paturzo ◽  
Pietro Ferraro
Keyword(s):  
Three Dimensional ◽  
Scene Reconstruction
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