SSP based underwater CIR estimation with S-BiFPN

Abstract

In underwater sensor networks (USN), channel impulse response (CIR) estimation based on sound speed profile (SSP) ensures link reliability. We propose a separate bi-directional feature pyramid network (S-BiFPN) that estimates the CIR using deep learning based on SSP. The proposed method enlarges the small variation of SSP by converting 1-dimension into 2-dimension, extracts various features using a fused feature map obtained from the separate paths, and estimates the CIR. Simulations are performed using the practically measured SSPs and CIRs, and the results show that the proposed method has the lowest mean square error (MSE) and a reasonable running time compared to conventional methods.

Keywords

• Machine learning
• CIR estimation
• SSP
• BiFPN