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Architecture, Applications and Challenges of Communication and Navigation Integration
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Architecture, Applications and Challenges of Communication and Navigation Integration

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".

Deadline for manuscript submissions: 25 December 2026 | Viewed by 2550

Special Issue Editor

Prof. Dr. Zhongliang Deng

E-Mail Website
Guest Editor
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: sensors; smartphones; scene sensing; positioning systems; 5G positioning; feature fusion; satellite communications; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of communication and navigation has rapidly become popular and has been applied in multiple industry fields, including intelligent transportation, logistics transportation management, emergency rescue, drones, autonomous driving and intelligent location services. The communication and navigation fusion system utilizes advanced algorithms and signal processing technologies to achieve high-performance location services while ensuring efficient communication. This Special Issue aims to explore the architecture, applications and challenges of communication and navigation fusion, and also delves into the potential of breakthrough technologies such as joint sensing and communication in improving positioning accuracy.

Prof. Dr. Zhongliang Deng
Guest Editor

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wireless positioning signal design and signal processing
  • high-precision distance measurement
  • high-precision angle measurement
  • wireless positioning signal propagation path identification
  • wireless positioning error estimation
  • multi-sensor fusion positioning
  • fusion positioning algorithm
  • communication and positioning fusion system
  • seamless indoor and outdoor location service
  • mobile device tracking

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Published Papers (3 papers)

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Research

19 pages, 4035 KB  
Article
An Improved Two-Stage RARE Algorithm for Mixed Far-Field and Near-Field Source Localization Under Unknown Mutual Coupling with the Uniform Linear Sensor Array
by Keyu Chen, Ke Deng and Jianguo Zhang
Sensors 2026, 26(3), 839; https://doi.org/10.3390/s26030839 - 27 Jan 2026
Cited by 1 | Viewed by 513
Abstract
An Improved Two-Stage Rank Reduction (ITS-RARE) algorithm is proposed for the localization of mixed far-field (FF) and near-field (NF) sources under unknown mutual coupling with the uniform linear sensor array. Our algorithm includes two steps: in the first step, the eigenvectors are exploited [...] Read more.
An Improved Two-Stage Rank Reduction (ITS-RARE) algorithm is proposed for the localization of mixed far-field (FF) and near-field (NF) sources under unknown mutual coupling with the uniform linear sensor array. Our algorithm includes two steps: in the first step, the eigenvectors are exploited when the rank reduction occurs at the right DOAs in our method. The eigenvectors corresponding to the smallest eigenvalues inherently represent the mutual coupling coefficient vectors. Based on it, the joint estimation of FF source DOAs and mutual coupling factors is achieved without pre-calibration. In the second step, after the DOA estimation of NF sources (NFSs), the ranges are estimated in closed form. As a result, the computational complexity is significantly reduced compared to existing methods. Furthermore, the full array aperture is preserved through the covariance matrix reconstruction (CMR) method during the FF/NF source classification. The simulation results demonstrate that the proposed algorithm is not only computationally efficient and effective in source classification but also preserves a larger effective aperture, thereby improving estimation accuracy. Full article
(This article belongs to the Special Issue Architecture, Applications and Challenges of Communication and Navigation Integration)
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33 pages, 11440 KB  
Article
A Vision-Assisted Acoustic Channel Modeling Framework for Smartphone Indoor Localization
by Can Xue, Huixin Zhuge and Zhi Wang
Sensors 2026, 26(2), 717; https://doi.org/10.3390/s26020717 - 21 Jan 2026
Viewed by 446
Abstract
Conventional acoustic time-of-arrival (TOA) estimation in complex indoor environments is highly susceptible to multipath reflections and occlusions, resulting in unstable measurements and limited physical interpretability. This paper presents a smartphone-based indoor localization method built on vision-assisted acoustic channel modeling, and develops a fusion [...] Read more.
Conventional acoustic time-of-arrival (TOA) estimation in complex indoor environments is highly susceptible to multipath reflections and occlusions, resulting in unstable measurements and limited physical interpretability. This paper presents a smartphone-based indoor localization method built on vision-assisted acoustic channel modeling, and develops a fusion anchor integrating a pan–tilt–zoom (PTZ) camera and a near-ultrasonic signal transmitter to explicitly perceive indoor geometry, surface materials, and occlusion patterns. First, vision-derived priors are constructed on the anchor side based on line-of-sight reachability, orientation consistency, and directional risk, and are converted into soft anchor weights to suppress the impact of occlusion and pointing mismatch. Second, planar geometry and material cues reconstructed from camera images are used to generate probabilistic room impulse response (RIR) priors that cover the direct path and first-order reflections, where environmental uncertainty is mapped into path-dependent arrival-time variances and prior probabilities. Finally, under the RIR prior constraints, a path-wise posterior distribution is built from matched-filter outputs, and an adaptive fusion strategy is applied to switch between maximum a posteriori (MAP) and minimum mean square error (MMSE) estimators, yielding debiased TOA measurements with calibratable variances for downstream localization filters. Experiments in representative complex indoor scenarios demonstrate mean localization errors of 0.096 m and 0.115 m in static and dynamic tests, respectively, indicating improved accuracy and robustness over conventional TOA estimation. Full article
(This article belongs to the Special Issue Architecture, Applications and Challenges of Communication and Navigation Integration)
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31 pages, 5219 KB  
Article
A Fault-Tolerant Localization Method for 5G/INS Based on Variational Bayesian Strong Tracking Fusion Filtering with Multilevel Fault Detection
by Zhongliang Deng, Ziyao Ma, Haiming Luo, Jilong Guo and Zidu Tian
Sensors 2025, 25(12), 3753; https://doi.org/10.3390/s25123753 - 16 Jun 2025
Cited by 2 | Viewed by 1072
Abstract
In this paper, for the needs of high-precision and high-continuity localization in complex environments, a modeling method based on time-varying noise and outlier noise is proposed, and variational Bayesian strong tracking filtering is used for 5G/INS fusion localization. A hierarchical progressive fault detection [...] Read more.
In this paper, for the needs of high-precision and high-continuity localization in complex environments, a modeling method based on time-varying noise and outlier noise is proposed, and variational Bayesian strong tracking filtering is used for 5G/INS fusion localization. A hierarchical progressive fault detection mechanism is proposed to detect IMU rationality faults and consistency faults in 5G observation information. The main contributions are reflected in the following two aspects: first, by innovatively introducing Pearson VII-type distribution for noise modeling, dynamically adjusting the tail thickness characteristics of the probability density function through its shape parameter, and effectively capturing the distribution law of extreme values in the observation data. Afterward, this article combined the variational Bayesian strong tracking filtering algorithm to construct a robust state estimation framework, significantly improving the localization accuracy and continuity in non-Gaussian noise environments. Second, a hierarchical progressive fault detection mechanism is designed. A wavelet fault detection method based on a hierarchical voting mechanism is adopted for IMU data to extract the abrupt features of the observed data and quickly identify faults. In addition, a dual-channel consistency detection model with dynamic fault-tolerant management was constructed. Sudden and gradual faults were quickly detected through a dual-channel pre-check, and then, the fault source was identified through AIME. Based on the fault source detection results, corresponding compensation mechanisms were adopted to achieve robust continuous localization. Full article
(This article belongs to the Special Issue Architecture, Applications and Challenges of Communication and Navigation Integration)
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