Journal article
Support-Constrained Mixed-Norm Optimization Techniques for Estimating Multipath Activity in Shallow Water Acoustic Channels
IEEE journal of oceanic engineering, Vol.45(3), pp.683-698
07/2020
DOI: 10.1109/JOE.2020.2980154
Abstract
In this article, we propose channel estimation techniques for shallow water acoustic channels that exhibit rapidly fluctuating high-amplitude multipath activity across different regions of the channel support. Specifically, we impose support constraints on the channel impulse response that confine tracking the shallow water acoustic channel to within the most active regions within the channel delay spread. The key idea is to focus on channel estimation performance through the banded nature of the multipath arrivals in the delay versus time channel that we denote as subregions of the channel delay spread. Because we are examining sparse shallow water acoustic channels, we adopt the well-known Lasso metric and related mixed-norm optimization techniques, but in contrast to traditional sparse sensing approaches, we use masked constraints to prioritize the estimation of regions of interest within the channel delay spread. This provides a tradeoff between computational time and estimation performance that we explore in detail over experimental field data from the SPACE08 experiment and over simulated channels emulating diverse oceanic conditions.
Details
- Title: Subtitle
- Support-Constrained Mixed-Norm Optimization Techniques for Estimating Multipath Activity in Shallow Water Acoustic Channels
- Creators
- Ryan A McCarthy - University of IowaAnanya Sen Gupta - University of IowaEmma Hawk - University of Iowa
- Resource Type
- Journal article
- Publication Details
- IEEE journal of oceanic engineering, Vol.45(3), pp.683-698
- Publisher
- IEEE
- DOI
- 10.1109/JOE.2020.2980154
- ISSN
- 0364-9059
- eISSN
- 1558-1691
- Grant note
- Iowa Center for Undergraduate Research N00014-18-1-2081; N00014-19-1-2436; N00014-19-1-2609 / Office of Naval Research (10.13039/100000006)
- Language
- English
- Date published
- 07/2020
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197531202771
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