New Techniques of Multiple Maneuvering Targets Passive Tracking by Single Observer Based on Information Fusion

2006 ◽  
Author(s):  
Jiegui Wang ◽  
Jingqing Luo
Keyword(s):  
Information Fusion ◽  
New Techniques ◽  
Maneuvering Targets ◽  
Passive Tracking

scholarly journals Maneuvering target recognition method based on multi-perspective light field reconstruction

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771987065 ◽  
Author(s):  
Lei Cai ◽  
Peien Luo ◽  
Guangfu Zhou ◽  
Zhenxue Chen
Keyword(s):  
Information Fusion ◽  
Light Field ◽  
Target Recognition ◽  
Multiple Perspectives ◽  
Distributed Information ◽  
Field Reconstruction ◽  
Maneuvering Target ◽  
Maneuvering Targets ◽  
Micro Lens Array ◽  
Bidirectional Reflectance Distribution

It is difficult to reconstruct the complete light field, and the reconstructed light field can only recognize specific fixed targets. These have limited the applications of the light field in practice. To solve the problems above, this article introduces the multi-perspective distributed information fusion into light field reconstruction to monitor and recognize the maneuvering targets. First, the light field is represented as sub-light fields at different perspectives (i.e. the Multi-sensor distributed network), and sparse representation and reconstruction are then performed. Second, we establish the multi-perspective distributed information fusion under the condition of regional full-coverage constraints. Finally, the light field data from multiple perspectives are fused and the states of the maneuvering targets are estimated. Experimental results show that the light field reconstruction time of the proposed method is less than 583 s, and the reconstruction accuracy exceeds 92.447% compared with the existing spatially variable bidirectional reflectance distribution function, micro-lens array, and others. In the aspect of maneuvering target recognition, the recognition time of the algorithm in this article is no more than 3.5 s. The recognition accuracy of the algorithm in this article is up to 86.739%. Moreover, the more viewing angles used, the higher the accuracy.

Passive tracking of underwater maneuvering targets

1978 ◽  
Author(s):  
R. Moose ◽  
D. McCabe ◽  
H. VanLandingham
Keyword(s):  
Maneuvering Targets ◽  
Passive Tracking

Airborne Single Observer Passive Tracking Algorithm Based on Improved CS Model

2012 ◽  
Vol 157-158 ◽  
pp. 136-139
Author(s):  
Wang Xi Li ◽  
Chang Qiang Huang ◽  
Yong Wang ◽  
Yong Bo Xuan
Keyword(s):  
Monte Carlo ◽  
Statistical Model ◽  
Monte Carlo Simulations ◽  
Tracking Algorithm ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Filtering Algorithm ◽  
Fuzzy Membership Functions ◽  
Maneuvering Targets ◽  
Passive Tracking

Aim at solving the problem that the target may exceed acceleration limit, a new passive tracking filtering algorithm based on “Current” Statistical (CS) model has been proposed. By designing nonlinear fuzzy membership functions, the new “Current” Statistical model filtering algorithm can adaptively adjust the acceleration upper and lower of CS model. Monte Carlo simulations of maneuvering targets show that the NCS algorithm has a better performance than the traditional CS algorithm.

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