Location-Aided Maximal Ratio Combining for an Acoustic Vector Sensor in Multipath Channels

The multi-channel outputs of an acoustic vector sensor (AVS) provide diversity gain for communications, and developing an effective combining scheme becomes a critical issue. However, in underwater multipath channels, a single AVS struggles to estimate the spatial signatures of multipath signals due to its limited sensing capability, which compromises the design of optimal combining weights. To overcome this issue, we propose a location-aided maximal ratio combining (MRC) technique. Armed with a predictable end-to-end propagation model, we first develop a maximum-likelihood sensing framework with the help of the pilot subcarriers embedded in the OFDM signal. The required channel state information is inferred from the estimated propagation geometry. Then, the combining weight vector is determined according to the MRC principle. Simulations demonstrate that this integrated scheme enhances communication performance through comprehensive environmental sensing.

Authors

Institutions

Publication Details

Journal
IEEE Signal Processing Letters
Published
2025-01-01
DOI
https://doi.org/10.1109/lsp.2025.3597557
Citations
2
Primary Topic
Target Tracking and Data Fusion in Sensor Networks
Type
article
Field-Weighted Citation Impact
4.96
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Location-Aided Maximal Ratio Combining for an Acoustic Vector Sensor in Multipath Channels

Xinghao Qu, Gang Qiao, Yiwen Zhou, Zhigang Shang
2 citations
IEEE Signal Processing Letters
Target Tracking and Data Fusion in Sensor Networks
4.96
article

Location-Aided Maximal Ratio Combining for an Acoustic Vector Sensor in Multipath Channels

Xinghao Qu, Gang Qiao, Yiwen Zhou, Zhigang Shang
article en
2 citations

Abstract

The multi-channel outputs of an acoustic vector sensor (AVS) provide diversity gain for communications, and developing an effective combining scheme becomes a critical issue. However, in underwater multipath channels, a single AVS struggles to estimate the spatial signatures of multipath signals due to its limited sensing capability, which compromises the design of optimal combining weights. To overcome this issue, we propose a location-aided maximal ratio combining (MRC) technique. Armed with a predictable end-to-end propagation model, we first develop a maximum-likelihood sensing framework with the help of the pilot subcarriers embedded in the OFDM signal. The required channel state information is inferred from the estimated propagation geometry. Then, the combining weight vector is determined according to the MRC principle. Simulations demonstrate that this integrated scheme enhances communication performance through comprehensive environmental sensing.

IEEE Signal Processing LettersVol. 32
Harbin Engineering University (CN)
Affordable and clean energy
Openalex Percentile: Top 5%
Target Tracking and Data Fusion in Sensor Networks
4.96
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Location-Aided Maximal Ratio Combining for an Acoustic Vector Sensor in Multipath Channels — Xinghao Qu, Gang Qiao, et al. · IEEE Signal Processing Letters (2025) | TGRS Research Map | TGRS