Abstract
In this paper, the relationship between the fusion result and the number of sensor tracks taking part in fusion is investigated, which reveals that it may be better to fuse many instead of all of the sensor tracks at hand. This result is interesting because at present, most approaches fuse all the available sensor tracks and treat all sensor data equally without regard of their different quality and different contribution to the system tracks. Then, in order to show that the appropriate sensor tracks for a fusion can be effectively selected from a set of available sensor tracks, an approach named STF is presented. STF is based on a two-stage paradigm of heuristic function construction and track state estimation fusion. The outliers in the tracks are eliminated by the orthogonal polynomial regression method at first. Then heuristic function is constructed by evaluating the quality of each track using grey correlation degree. Last, the track state estimation fusion is guided by the heuristic function, in which an optimal number of tracks are fused. In addition, the paper discusses its implementation in the multi-sensor and multi-target environment. The effectiveness and the superiority of STF are verified in experiment.
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Xu, L., Ma, P., Su, X. (2011). Selective Track Fusion. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_3
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DOI: https://doi.org/10.1007/978-3-642-24965-5_3
Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-642-24965-5
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