Blind Deinterleaving of Signals in Time Series with Self-Attention Based Soft Min-Cost Flow Learning

Date

2021-01-01

Author

Can, Oğul
Gürbüz, Yeti Ziya
Yildirim, Berkin
Alatan, Abdullah Aydın

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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We propose an end-to-end learning approach to address deinterleaving of patterns in time series, in particular, radar signals. We link signal clustering problem to min-cost flow as an equivalent problem once the proper costs exist. We formulate a bi-level optimization problem involving min-cost flow as a sub-problem to learn such costs from the supervised training data. We then approximate the lower level optimization problem by self-attention based neural networks and provide a trainable framework that clusters the patterns in the input as the distinct flows. We evaluate our method with extensive experiments on a large dataset with several challenging scenarios to show the efficiency.

Subject Keywords

Deinterleaving, attention, min-cost flow, IMPROVED ALGORITHM

URI

https://hdl.handle.net/11511/94724

DOI

https://doi.org/10.1109/icassp39728.2021.9415025

Conference Name

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

O. Can, Y. Z. Gürbüz, B. Yildirim, and A. A. Alatan, “Blind Deinterleaving of Signals in Time Series with Self-Attention Based Soft Min-Cost Flow Learning,” presented at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ELECTR NETWORK, 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/94724.