A Gravity Matching Adaptability Analysis Method Based on Gravity Directional Characteristics

TY - JOUR

T1 - A Gravity Matching Adaptability Analysis Method Based on Gravity Directional Characteristics

AU - Zhang, Wenzhe

AU - Wang, Yu

AU - Zhao, Shengwu

AU - Deng, Zhihong

N1 - Publisher Copyright: © 2026 IEEE. All rights reserved.

PY - 2026/4/1

Y1 - 2026/4/1

N2 - Gravity-aided inertial navigation system (GAINS) is one of the key technologies for underwater navigation and positioning. The position accuracy relates to the spatial variability and directional characteristics of the gravity anomaly map. To quantify the directional adaptability of gravity anomaly maps, this article proposes a novel analysis method based on gravity directional characteristics (GDCs). First, three key parameters are designed: gravity profile similarity, directional gravity gradient, and directional gradient concentration, which characterize the directional distribution of gravity anomalies. Then, a balanced gravity characteristic (BGC) parameter is constructed by fusing classical gravity metrics. The mapping between these features and directional matching rates (MRs) is then modeled using an LM-BP neural network, establishing an adaptability evaluation framework. Experimental results demonstrate that in regions with directional adaptability exceeding 0.8, the MRs are over 80%. The proposed model offers higher accuracy and comprehensiveness in selecting gravity-matching areas than existing methods and provides a practical tool for optimizing gravity matching accuracy.

AB - Gravity-aided inertial navigation system (GAINS) is one of the key technologies for underwater navigation and positioning. The position accuracy relates to the spatial variability and directional characteristics of the gravity anomaly map. To quantify the directional adaptability of gravity anomaly maps, this article proposes a novel analysis method based on gravity directional characteristics (GDCs). First, three key parameters are designed: gravity profile similarity, directional gravity gradient, and directional gradient concentration, which characterize the directional distribution of gravity anomalies. Then, a balanced gravity characteristic (BGC) parameter is constructed by fusing classical gravity metrics. The mapping between these features and directional matching rates (MRs) is then modeled using an LM-BP neural network, establishing an adaptability evaluation framework. Experimental results demonstrate that in regions with directional adaptability exceeding 0.8, the MRs are over 80%. The proposed model offers higher accuracy and comprehensiveness in selecting gravity-matching areas than existing methods and provides a practical tool for optimizing gravity matching accuracy.

KW - Adaptability analysis

KW - gravity directional characteristic (GDC)

KW - gravity-aided inertial navigation system (INS)

KW - matching area selection

UR - https://www.scopus.com/pages/publications/105031524595

U2 - 10.1109/JSEN.2026.3665274

DO - 10.1109/JSEN.2026.3665274

M3 - Article

AN - SCOPUS:105031524595

SN - 1530-437X

VL - 26

SP - 10731

EP - 10744

JO - IEEE Sensors Journal

JF - IEEE Sensors Journal

IS - 7

ER -