Name
Title | ||
|---|---|---|
Uncertain Inference Using Ordinal Classification in Deep Networks for Acoustic Localization |
Abstract | ||
|---|---|---|
Highly-reverberate underwater environments pose challenges for conventional localization techniques due to the highly non-linear nature of reflective surfaces, multi-path, and scattering fields. In this paper, we compare different machine learning methods for passive localization and tracking of single, non-stationary, underwater acoustic sources using multiple underwater acoustic vector sensors. We incorporate ordinal classification for localization in a novel approach to acoustic localization and compare the results with other standard methods. Real-world experiments demonstrate that both categorical and ordinal classification using deep LSTM networks significantly reduce localization error. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9533605 | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
Keywords | DocType | ISSN |
underwater acoustics, localization, machine learning, LSTMs, ordinal classification | Conference | 2161-4393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steven Whitaker | 1 | 0 | 0.34 |
| Zach Dekraker | 2 | 0 | 0.34 |
| Andrew Barnard | 3 | 0 | 0.68 |
| Timothy C. Havens | 4 | 2 | 1.42 |
| George D. Anderson | 5 | 0 | 0.34 |