Abstract
This paper derives an algorithm for multi-sensor asynchronous track-to-track fusion that combines tracks provided by different sensors, which have each communication delay. In this algorithm, an adaptive approach for fusion in a multi-sensors environment is used. The measurements of two sensors tracking the same target are processed by linear Kalman Filters, and the outputs of the local trackers are sent to the central node. In this node, a decision logic, which is based on the comparison between distance metrics and thresholds, selects the method to obtain the global estimate. The simulation result illustrates that this algorithms approaches the Weighted Covariance Fusion (WCF) algorithm in the fusion precision, and the computational burden reduces one about the half.
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Cheng, C., Wang, J. (2009). Algorithm for Multi-sensor Asynchronous Track-to-Track Fusion. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_92
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DOI: https://doi.org/10.1007/978-3-642-01513-7_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01512-0
Online ISBN: 978-3-642-01513-7
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