Underwater Sound Speed Profile Construction: A Review

TL;DR

This review discusses methods for constructing underwater sound speed profiles, comparing direct measurement and inversion techniques, highlighting their advantages, limitations, and future research directions for real-time, accurate SSP estimation.

Contribution

It provides a comprehensive analysis of current SSP construction methods, including recent advances in inversion techniques like deep learning, and discusses future trends in multi-source data utilization.

Findings

Direct measurement offers high accuracy but is slow.

Inversion methods improve real-time performance but have lower accuracy.

Future research focuses on multi-source data integration for better SSP estimation.

Abstract

Real--time and accurate construction of regional sound speed profiles (SSP) is important for building underwater positioning, navigation, and timing (PNT) systems as it greatly affect the signal propagation modes such as trajectory. In this paper, we summarizes and analyzes the current research status in the field of underwater SSP construction, and the mainstream methods include direct SSP measurement and SSP inversion. In the direct measurement method, we compare the performance of popular international commercial temperature, conductivity, and depth profilers (CTD). While for the inversion methods, the framework and basic principles of matched field processing (MFP), compressive sensing (CS), and deep learning (DL) for constructing SSP are introduced, and their advantages and disadvantages are compared. The traditional direct measurement method has good accuracy performance, but it usually takes a long time. The proposal of SSP inversion method greatly improves the convenience and real--time performance, but the accuracy is not as good as the direct measurement method. Currently, the SSP inversion relies on sonar observation data, making it difficult to apply to areas that couldn't be covered by underwater observation systems, and these methods are unable to predict the distribution of sound velocity at future times. How to comprehensively utilize multi-source data and provide elastic sound velocity distribution estimation services with different accuracy and real-time requirements for underwater users without sonar observation data is the mainstream trend in future research on SSP construction.