Multi-Source Confidence Assessment-Based Adaptive Calibration for Deep-Sea Manned Submersible Integrated Navigation

TL;DR

This paper introduces a new adaptive navigation method for deep-sea submersibles that improves reliability in challenging underwater environments.

Contribution

A novel four-dimensional confidence assessment framework for USBL systems is proposed to adaptively adjust navigation weights in real time.

Findings

The proposed method achieves an average position error of 1.15 m in harsh deep-sea conditions.

It improves navigation reliability by 53.1% compared to conventional methods.

Jump point anomalies see a 58.9% improvement in error reduction.

Abstract

To address the insufficient reliability of manned submersible navigation systems in complex deep-sea environments, this paper proposes an adaptive fusion navigation method based on multi-dimensional confidence assessment. This study proposes a method establishing a four-dimensional evaluation framework for the USBL (Ultra-Short Baseline) positioning system. The framework encompasses signal quality, geometric precision, environmental attenuation, and data stability. It enables the quantitative, real-time assessment of system reliability. Consequently, it facilitates an adaptive weight adjustment mechanism. Experimental results demonstrate that under harsh conditions featuring jump point anomalies and data loss, the proposed algorithm achieves an average position error of 1.15 m. This represents a 53.1% improvement over conventional methods, with the enhancement reaching 58.9% in scenarios specifically affected by jump points. The proposed method study effectively enhances the navigation reliability of manned submersibles in complex underwater acoustic environments, thereby demonstrating significant engineering application value.

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