Next Article in Journal
Inverse Problem of Identifying a Time-Dependent Source Term in a Fractional Degenerate Semi-Linear Parabolic Equation
Previous Article in Journal
A Novel Hybrid Approach Using an Attention-Based Transformer + GRU Model for Predicting Cryptocurrency Prices
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Review

Advances in Multi-Source Navigation Data Fusion Processing Methods

by
Xiaping Ma
1,
Peimin Zhou
1,* and
Xiaoxing He
2
1
School of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
2
School of Civil Engineering and Surveying and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
*
Author to whom correspondence should be addressed.
Mathematics 2025, 13(9), 1485; https://doi.org/10.3390/math13091485
Submission received: 12 March 2025 / Revised: 25 April 2025 / Accepted: 28 April 2025 / Published: 30 April 2025

Abstract

In recent years, the field of multi-source navigation data fusion has witnessed substantial advancements, propelled by the rapid development of multi-sensor technologies, Artificial Intelligence (AI) algorithms and enhanced computational capabilities. On one hand, fusion methods based on filtering theory, such as Kalman Filtering (KF), Particle Filtering (PF), and Federated Filtering (FF), have been continuously optimized, enabling effective handling of non-linear and non-Gaussian noise issues. On the other hand, the introduction of AI technologies like deep learning and reinforcement learning has provided new solutions for multi-source data fusion, particularly enhancing adaptive capabilities in complex and dynamic environments. Additionally, methods based on Factor Graph Optimization (FGO) have also demonstrated advantages in multi-source data fusion, offering better handling of global consistency problems. In the future, with the widespread adoption of technologies such as 5G, the Internet of Things, and edge computing, multi-source navigation data fusion is expected to evolve towards real-time processing, intelligence, and distributed systems. So far, fusion methods mainly include optimal estimation methods, filtering methods, uncertain reasoning methods, Multiple Model Estimation (MME), AI, and so on. To analyze the performance of these methods and provide a reliable theoretical reference and basis for the design and development of a multi-source data fusion system, this paper summarizes the characteristics of these fusion methods and their corresponding application scenarios. These results can provide references for theoretical research, system development, and application in the fields of autonomous driving, unmanned vehicle navigation, and intelligent navigation.
Keywords: multi-source navigation; fusion processing; LSE; KF; PF; FG; MME multi-source navigation; fusion processing; LSE; KF; PF; FG; MME

Share and Cite

MDPI and ACS Style

Ma, X.; Zhou, P.; He, X. Advances in Multi-Source Navigation Data Fusion Processing Methods. Mathematics 2025, 13, 1485. https://doi.org/10.3390/math13091485

AMA Style

Ma X, Zhou P, He X. Advances in Multi-Source Navigation Data Fusion Processing Methods. Mathematics. 2025; 13(9):1485. https://doi.org/10.3390/math13091485

Chicago/Turabian Style

Ma, Xiaping, Peimin Zhou, and Xiaoxing He. 2025. "Advances in Multi-Source Navigation Data Fusion Processing Methods" Mathematics 13, no. 9: 1485. https://doi.org/10.3390/math13091485

APA Style

Ma, X., Zhou, P., & He, X. (2025). Advances in Multi-Source Navigation Data Fusion Processing Methods. Mathematics, 13(9), 1485. https://doi.org/10.3390/math13091485

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop