A track association algorithm based on the weighted association graph for laser triangulation sensors

Xu, Liang; Jin, Shuilin; Yin, Guisheng

doi:10.1016/j.ijleo.2014.07.034

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…For the distributed tracking system, due to the lack of information about radar overlapping and the number of targets in the FC, one of the core problems to be solved is to merge the duplicate tracks. The process of determining the duplicate tracks is called track-to-track association (TTTA) [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Track-to-Track Association Based on Pseudo Nearest Neighbor Distance for Distributed Networked Radar System

Chen

Zhang

et al. 2023

Electronics

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In radar network systems, target tracks reported from different radars need to be associated and fused, and the track-to-track association (TTTA) effect is a key factor that directly affects the performance of the entire system. In order to solve the problem of the low accuracy of TTTA in network radar systems with asynchronous unequal rates, an asynchronous TTTA algorithm based on pseudo nearest neighbor distance is proposed. Firstly, the calculation method of pseudo nearest neighbor distance between the track point and the track data set is defined, then the correlation degree between the two track data sets is obtained by using grey theory, and then the Jonker-Volgenant algorithm is combined with the classical allocation method to judge the TTTA. The algorithm does not need time domain alignment and can effectively avoid the accumulation and propagation of estimation errors. The simulation results show that the algorithm has a high average correct association rate and is less affected by the radar sampling period ratio, startup time, and noise distribution, and the average correct association rate for different movement types of target tracks remains above 99%. Furthermore, compared with other algorithms, this algorithm maintains a stable low level of the number of false associations and the maximum false association rates, and has strong robustness and advantages.

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Section: Introductionmentioning

confidence: 99%

Asynchronous Track-to-Track Association Based on Pseudo Nearest Neighbor Distance for Distributed Networked Radar System

Chen

Zhang

et al. 2023

Electronics

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“…Multi-sensor tracking systems [1,2] have become a hot research field in recent years because they can expand the information acquisition range, improve the accuracy and reliability of reconnaissance systems, and improve the stability of target track and information [3,4]. The distributed multi-sensor tracking system has become the preferred solution for research in this field due to its advantages such as low communication bandwidth requirements, low computational complexity, and high survivability [5,6], and how to judge tracks from different sensors as the same target is one of the core problems to be solved, that is, TTTA [7][8][9][10][11]. Typical tracks include the flight path of aerial targets, the navigation path of naval vessels, etc.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Anti-Bias Track-to-Track Association Algorithm Based on Nearest Neighbor Interval Average Distance for Multi-Sensor Tracking Systems

Chen

Zhang

et al. 2023

Electronics

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Due to sensor characteristics, geographical environment, electromagnetic interference, electromagnetic silence, information countermeasures, and other reasons, there may be significant system errors in sensors in multi-sensor tracking systems, resulting in poor track-to-track association (TTTA) effect of the system. In order to solve the problem of TTTA under large system errors, this paper proposes an asynchronous anti-bias TTTA algorithm that utilizes the average distance between the nearest neighbor intervals between tracks. This algorithm proposes a systematic error interval processing method to track coordinates, and then defines the nearest neighbor interval average distance between interval coordinate datasets and interval coordinate points, and then uses grey theory to calculate the correlation degree between tracks. Finally, the Jonker–Volgenant algorithm is combined to use the canonical allocation method for TTTA judgment. The algorithm requires less prior information and does not require error registration. The simulation results show that the algorithm can ensure a high average correct association rate (over 98%) of asynchronous unequal rate tracks under large system errors, and achieve stable association, with good association and anti-bias performance. Compared with other algorithms, the algorithm maintains good performance for different target numbers and processing cycles, and has good superiority and robustness.

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A Track Association Method Based on Uncertainty Distribution

Long¹,

Cai²,

Li³

et al. 2023

Lecture Notes in Electrical Engineering

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A track association algorithm based on the weighted association graph for laser triangulation sensors

Cited by 3 publications

References 13 publications

Asynchronous Track-to-Track Association Based on Pseudo Nearest Neighbor Distance for Distributed Networked Radar System

Asynchronous Track-to-Track Association Based on Pseudo Nearest Neighbor Distance for Distributed Networked Radar System

Asynchronous Anti-Bias Track-to-Track Association Algorithm Based on Nearest Neighbor Interval Average Distance for Multi-Sensor Tracking Systems

A Track Association Method Based on Uncertainty Distribution

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