Group Tracking of Flock Targets in Low-Altitude Airspace

Chen, Weishi; Liu, Hong; Hu, Sha; Ning, Huansheng

doi:10.1109/ispaw.2011.10

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…In existing literatures, K-Means algorithm is usually used to implement group division [26]. However, this algorithm usually requires the number of groups in advance, and has the problem of slow convergence speed, which is not convenient for practical use.…”

Section: ) a Novel Group Division Methods With Round-nnamentioning

confidence: 99%

Robust Tracking for Small Target Groups With Dynamic Outlines

Zhou

Wang

2020

IEEE Access

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Low altitude small target group has the special motion characteristics of orderly as a whole and flexibly as individuals. The performances of traditional target tracking algorithms (such as centroid tracking, geometric centre tracking, etc.) decrease obviously when the group outline changes. Hence in this paper, a robust tracking algorithm for target group with dynamic outline is studied. Aiming at the two key issues of group division and equivalent tracking point estimation, a robust group division algorithm, which can maintain the stability of group division results under any reference point selection, is firstly introduced. Then, on this basis, a new optimal tracking point estimation method is proposed by introducing the group structure into the likelihood function. The simulation results show that this proposed method can effectively improve the robustness of low altitude small target group tracking in the scene of group merging and group splitting. INDEX TERMS Dynamic outlines, equivalent point estimation, group division, group tracking.

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Section: ) a Novel Group Division Methods With Round-nnamentioning

confidence: 99%

Robust Tracking for Small Target Groups With Dynamic Outlines

Zhou

Wang

2020

IEEE Access

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“…At present, with the development of the wireless communication, embedded technology, low power consumption system on chip, and sensing technology, wireless sensor networks (WSNs) with the computing power, communication capabilities and awareness have become increasingly common and have gained a mushroom growth in military defense, environmental monitoring, disaster forecasting , intelligent transportation, and many other applications. Moving target tracking is a basic function in many applications in WSNs . For example, location and tracking of the air or ground target need to be supported by mobile target tracking technology in military surveillance.…”

Section: Introductionmentioning

confidence: 99%

Track fusion based on threshold factor classification algorithm in wireless sensor networks

Wang

Chen

et al. 2016

Int J Communication

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Summary Traditional tracking classification algorithm has been widely applied to target tracking in wireless sensor networks. In this paper, focusing on the accuracy of target tracking in wireless sensor networks, we propose an improved threshold factor track classification algorithm. The algorithm extracts the motion model according to the intrinsic properties of the target. It updates the iterative center according to the real‐time motion state of the moving target and timely filters out the weak correlated or uncorrelated data. In order to show the improved threshold factor classification algorithm is more effective, we compare the proposed algorithm with the classification algorithm based on the Euclidean distance comprehensive function. Experimental results show that through the proposed algorithm, the mean error and variance in the direction of x/y/z have been reduced to a certain extent, and the computation time consumed is also reduced. Copyright © 2016 John Wiley & Sons, Ltd.

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“…In [6], the center of mass is used for group tracking. In [7], the tracking performance of geometric center and center of mass are compared. However, these methods are commonly based on engineering experiences, lacking of necessary theoretical analysis [7].…”

mentioning

confidence: 99%

“…In [7], the tracking performance of geometric center and center of mass are compared. However, these methods are commonly based on engineering experiences, lacking of necessary theoretical analysis [7]. Meanwhile, the structure information was ignored.…”

mentioning

confidence: 99%