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Asynchronous track-to-track association algorithm based on reference topology feature

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Abstract

In multi-sensor multi-target tracking systems, the different sensors provide their detections at different times due to having different sampling periods or communication delays. Therefore, for track-to-track association algorithms, it is not realistic to assume that sensors are synchronous. In this paper, we propose an asynchronous track-to-track association algorithm based on reference topology feature. For synchronization of sensor tracks, a one-step, memoryless track propagation scheme is used. The proposed method handles both the asynchronous and synchronous sensors. An improved performance is achieved by using the generalized sub-patterns assignment metric to calculate the association costs between two reference topologies. The proposed algorithm also provides an improved performance for the synchronized sensors case.

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Notes

  1. There are two types of alignment: temporal alignment (synchronization) and spatial alignment (sensor bias estimation). Since the presented approach here bypasses the translational and range biases of the sensors, we have only considered the temporal alignment of the tracks across different sensors.

  2. The scenario and simulation parameters are selected, for a fair comparison against the other algorithms, as in the other papers, for each scenario. The simulations which are specific to this study, are run for 1000 Monte Carlo runs to show the statistical consistency of the proposed method, see Fig. 10.

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Acknowledgements

We thank the support extended by the Scientific and Technical Research Council of Turkey (TUBITAK) for this work. No opinion in this publication is the official view of TUBITAK.

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  1. Department of Electronics Engineering, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey

    Hasan Hüseyin Sönmez & Ali Köksal Hocaoğlu

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  1. Hasan Hüseyin Sönmez
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Correspondence to Ali Köksal Hocaoğlu.

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Sönmez, H.H., Hocaoğlu, A.K. Asynchronous track-to-track association algorithm based on reference topology feature. SIViP 16, 789–796 (2022). https://doi.org/10.1007/s11760-021-02019-9

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