Recent Developments in Statistical Signal Processing and Its Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 368

Special Issue Editor


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Guest Editor
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: statistical signal processinginformation fusionstate estimation

Special Issue Information

Dear Colleagues,

In recent years, advancements in sensor technology and manufacturing processes have led to the development of numerous low-cost, compact, low-power, and intelligent sensors. These innovations have created opportunities for capturing various types of sensor data. Additionally, improvements in information processing and computing capabilities have made it possible to perform complex statistical signal processing methods in real-time. While the proliferation of sensors and enhanced computing power offer significant advantages, they also present challenges in information processing and heighten the risk of encountering serious issues, such as malicious cyber-attacks. In complex and dynamic application environments, effectively integrating multi-source data that include complex noise characteristics and potential network threats has emerged as a major challenge.

This Special Issue will investigate the latest theoretical and practical advancements, as well as significant breakthroughs in the field of statistical information processing. It aims to promote the exchange of ideas and the development of signal processing technology. We look forward to your submissions.

Prof. Dr. Guoqing Wang
Guest Editor

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Keywords

  • advanced signal processing theories
  • multi-sensor information fusion
  • distributed state estimation
  • robust state estimation
  • adaptive state estimation
  • cyber attack detection and estimation
  • advanced signal processing applications
  • integrated navigation
  • cooperative navigation
  • simultaneous localization and mapping (SLAM)
  • all-source navigation
  • unmanned system navigation

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Published Papers (1 paper)

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Review

36 pages, 2990 KiB  
Review
Advances in Multi-Source Navigation Data Fusion Processing Methods
by Xiaping Ma, Peimin Zhou and Xiaoxing He
Mathematics 2025, 13(9), 1485; https://doi.org/10.3390/math13091485 - 30 Apr 2025
Viewed by 177
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 [...] Read more.
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. Full article
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