scholarly journals An Efficient Multi-Path Multitarget Tracking Algorithm for Over-The-Horizon Radar

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1384 ◽  
Author(s):  
Yuan Huang ◽  
Yifang Shi ◽  
Taek Song

In target tracking environments using over-the-horizon radar (OTHR), one target may generate multiple detections through different signal propagation paths. Trackers need to jointly handle the uncertainties stemming from both measurement origin and measurement path. Traditional multitarget tracking algorithms suffer from high computational loads in such environments since they need to enumerate all possible joint measurement-to-track assignments considering the measurements paths unless they employ some approximations regarding the measurements and their corresponding paths. In this paper, we propose a novel algorithm, named multi-path linear multitarget integrated probabilistic data association (MP-LM-IPDA), to efficiently track multitarget in multiple detection environments. Instead of generating all possible joint assignments, MP-LM-IPDA calculates the modulated clutter measurement density for each measurement cell of each track. The modulated clutter measurement density considers the possibility that the measurement cells originate from the clutter as well as from other potential targets. By incorporating the modulated clutter measurement density, the single target tracking structure can be applied for multitarget tracking, which significantly reduces the computational load. The simulation results demonstrate the effectiveness and efficiency of the proposed algorithm.

Author(s):  
Na An ◽  
Wei Qi Yan

In this article, we detect and track visual objects by using Siamese network or twin neural network. The Siamese network is constructed to classify moving objects based on the associations of object detection network and object tracking network, which are thought of as the two branches of the twin neural network. The proposed tracking method was designed for single-target tracking, which implements multitarget tracking by using deep neural networks and object detection. The contributions of this article are stated as follows. First, we implement the proposed method for visual object tracking based on multiclass classification using deep neural networks. Then, we attain multitarget tracking by combining the object detection network and the single-target tracking network. Next, we uplift the tracking performance by fusing the outcomes of the object detection network and object tracking network. Finally, we speculate on the object occlusion problem based on IoU and similarity score, which effectively diminish the influence of this issue in multitarget tracking.


2014 ◽  
Vol 496-500 ◽  
pp. 1564-1567
Author(s):  
Jing Feng He ◽  
Ming Ji ◽  
Song Cheng ◽  
Ya Nan Wang

Based on introducing the traditional scan and single target tracking state, focuses on the automatic tracking characteristics of each stage under the condition of multiple targets. The two form of automatic tracking multiple targets, and the development direction of the future.


Author(s):  
Xueting Li ◽  
Wei Yi ◽  
Guolong Cui ◽  
Lingjiang Kong ◽  
Xiaobo Yang

2003 ◽  
Author(s):  
Mark R. Morelande ◽  
Subhash Challa ◽  
Neil J. Gordon

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