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Electronics
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Recent Advance of Auto Navigation in Indoor Scenarios
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Recent Advance of Auto Navigation in Indoor Scenarios

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 17195

Special Issue Editors

Dr. Wenchao Zhang

E-Mail
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: indoor positioning algorithm; integrated navigation algorithm; multi-information fusion method
Special Issues, Collections and Topics in MDPI journals
Dr. Jian Kuang

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Guest Editor
GNSS Research Center, Wuhan University, Wuhan 430072, China
Interests: inertial navigation; magnetic positioning; pedestrian navigation; vehicle positioning; multi-source fusion positioning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Location-based services (LBS) play a crucial role in supporting people's work, travel, factory production, and other social activities, becoming an indispensable part of transportation, the economy, and society. Central to LBS is positioning, a fundamental technology that underpins its functionality. In recent years, the Global Navigation Satellite System (GNSS) has matured significantly, offering reliable meter-level or even centimeter-level location services around the clock in open outdoor environments. However, GNSS is unsuitable for shielded environments such as tunnels and indoor spaces. To complement GNSS, indoor positioning technologies have rapidly developed, including UWB, Wi-Fi, BLE, visible light, vision, magnetic field, inertial navigation, etc. Despite these advancements, GNSS-denied environments present challenges due to the complex and diverse indoor space structures, where a single indoor positioning technology may struggle with issues such as low availability, poor accuracy, high system costs, and limited coverage. Consequently, achieving wide-area, high-precision indoor positioning at a low cost has become a prominent and urgent research focus in the field of navigation and positioning.

This Special Issue aims to introduce the latest breakthroughs in the theoretical research, technological innovation, and practical application of autonomous navigation in indoor scenarios and its future development prospects. Topics of interest include, but are not limited to, the following:

  • Inertial navigation;
  • Pedestrian, vehicle, bicycle, and drone dead reckoning;
  • UWB, ultrasound, Wi-Fi, BLE, LED, visual, and magnetic positioning;
  • Attitude and heading reference system;
  • Gyroscope, accelerometer, and magnetometer calibration;
  • Data-driven positioning method;
  • Simultaneous localization and mapping (SLAM);
  • Crowdsourcing-based mapping;
  • Optimal estimation methods such as Kalman filtering and graph optimization.

Dr. Wenchao Zhang
Dr. Jian Kuang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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. Electronics 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 2400 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

  • indoor positioning
  • auto navigation
  • dead reckoning
  • matching positioning
  • multi-source fusion

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Published Papers (7 papers)

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Research

38 pages, 8935 KB  
Article
3D-IMB-APDR: Inertial-Geomagnetic-Barometric-Based Adaptive Infrastructure-Free 3D Pedestrian Dead Reckoning Method
by Tianqi Tian, Yanzhu Hu, Bin Hu, Yingjian Wang and Xinghao Zhao
Electronics 2026, 15(8), 1669; https://doi.org/10.3390/electronics15081669 - 16 Apr 2026
Viewed by 374
Abstract
With the rapid development of underground spaces and demand for infrastructure-independent autonomous positioning in post-disaster rescue, Pedestrian Dead Reckoning (PDR) has become a key research focus. However, traditional PDR suffers from cumulative heading drift, inadequate 3D positioning performance, and poor anti-magnetic interference capabilities, [...] Read more.
With the rapid development of underground spaces and demand for infrastructure-independent autonomous positioning in post-disaster rescue, Pedestrian Dead Reckoning (PDR) has become a key research focus. However, traditional PDR suffers from cumulative heading drift, inadequate 3D positioning performance, and poor anti-magnetic interference capabilities, failing to meet the high-precision positioning requirements of rescuers in underground and multistory buildings. To address these issues, this paper proposes an adaptive 3D-PDR method fusing inertial, geomagnetic, and barometric (3D-IMB-APDR). Sensor data are optimized via FFT dominant frequency extraction and Butterworth zero-phase filtering, with magnetic interference compensated by geomagnetic ellipse fitting. A segmental heading correction with a multi-criteria dynamic geomagnetic reliability model suppresses heading drift. A barometer-based coarse estimation and inertial fine correction architecture is adopted, where a lightweight CNN-BiLSTM network extracts inertial features for step height, and AEKF fuses multi-source data to achieve accurate vertical height estimation and precise 3D positioning. Validated in sports fields, underground parking garages, and staircases, the method outperforms four comparative methods, reducing positional RMSE by 65.77–98.23%, with endpoint errors of 1.40 m, 2.56 m, and 0.32 m, respectively. Relying solely on chest-worn sensors, it provides a reliable 3D autonomous positioning solution for rescuers in post-disaster rescue and underground engineering. Full article
(This article belongs to the Special Issue Recent Advance of Auto Navigation in Indoor Scenarios)
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20 pages, 5083 KB  
Article
MDR–SLAM: Robust 3D Mapping in Low-Texture Scenes with a Decoupled Approach and Temporal Filtering
by Kailin Zhang and Letao Zhou
Electronics 2025, 14(24), 4864; https://doi.org/10.3390/electronics14244864 - 10 Dec 2025
Viewed by 762
Abstract
Realizing real-time dense 3D reconstruction on resource-limited mobile platforms remains a significant challenge, particularly in low-texture environments that demand robust multi-frame fusion to resolve matching ambiguities. However, the inherent tight coupling of pose estimation and mapping in traditional monolithic SLAM architectures imposes a [...] Read more.
Realizing real-time dense 3D reconstruction on resource-limited mobile platforms remains a significant challenge, particularly in low-texture environments that demand robust multi-frame fusion to resolve matching ambiguities. However, the inherent tight coupling of pose estimation and mapping in traditional monolithic SLAM architectures imposes a severe restriction on integrating high-complexity fusion algorithms without compromising tracking stability. To overcome these limitations, this paper proposes MDR–SLAM, a modular and fully decoupled stereo framework. The system features a novel keyframe-driven temporal filter that synergizes efficient ELAS stereo matching with Kalman filtering to effectively accumulate geometric constraints, thereby enhancing reconstruction density in textureless areas. Furthermore, a confidence-based fusion backend is employed to incrementally maintain global map consistency and filter outliers. Quantitative evaluation on the NUFR-M3F indoor dataset demonstrates the effectiveness of the proposed method: compared to the standard single-frame baseline, MDR–SLAM reduces map RMSE by 83.3% (to 0.012 m) and global trajectory drift by 55.6%, while significantly improving map completeness. The system operates entirely on CPU resources with a stable 4.7 Hz mapping frequency, verifying its suitability for embedded mobile robotics. Full article
(This article belongs to the Special Issue Recent Advance of Auto Navigation in Indoor Scenarios)
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21 pages, 3536 KB  
Article
Batch Cyclic Posterior Selection Particle Filter and Its Application in TRN
by Zhiqiang Lyu, Xingzi Qiang, Wenwu Shi, Yingkui Gong and Longxing Wu
Electronics 2025, 14(21), 4257; https://doi.org/10.3390/electronics14214257 - 30 Oct 2025
Viewed by 610
Abstract
Terrain referenced navigation (TRN) determines position by comparing terrain height measurements with digital elevation maps (DEMs). However, terrain fluctuations create multimodal observation distributions, introducing significant nonlinearity that challenges fusion positioning algorithms. To address this, we propose a novel data fusion approach: batch cyclic [...] Read more.
Terrain referenced navigation (TRN) determines position by comparing terrain height measurements with digital elevation maps (DEMs). However, terrain fluctuations create multimodal observation distributions, introducing significant nonlinearity that challenges fusion positioning algorithms. To address this, we propose a novel data fusion approach: batch cyclic posterior selection particle filter (BCPS-PF), applied to TRN. Our algorithm consists of two primary mechanisms. First, the batch cycle particle generation mechanism continuously generates particles conforming to the prior distribution. This is achieved by decomposing the state transition function and the state noise model during the prediction step. Particles from the previous time step are transformed via the state transition function, and noise sequences generated by the state noise model are added, forming batch cycle particles. Second, a particle selection mechanism filters particles to match the posterior distribution. This involves an update step in the fusion process, utilizing a rejection sampling technique. The batch cycle mechanism can be terminated by limiting the number of particles, and state estimation is derived by calculating the mean of these particles. Simulations demonstrate that our method improves positioning accuracy by over 10% compared with existing methods. Full article
(This article belongs to the Special Issue Recent Advance of Auto Navigation in Indoor Scenarios)
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15 pages, 2671 KB  
Article
A Novel Integrated IMU-UWB Framework for Walking Trajectory Estimation in Non-Line-of-Sight Scenarios Involving Turning Gait
by Haonan Jia, Tongrui Peng, Wenchao Zhang, Qifei Fan, Zhikang Zhong, Hongsheng Li and Xinyao Hu
Electronics 2025, 14(17), 3546; https://doi.org/10.3390/electronics14173546 - 5 Sep 2025
Viewed by 1601
Abstract
Accurate walking trajectory estimation is critical for monitoring activity levels in healthcare and occupational safety applications. Ultra-Wideband (UWB) technology has emerged as a key solution for indoor human activity and trajectory tracking. However, its performance is fundamentally limited by Non-Line-of-Sight (NLOS) errors and [...] Read more.
Accurate walking trajectory estimation is critical for monitoring activity levels in healthcare and occupational safety applications. Ultra-Wideband (UWB) technology has emerged as a key solution for indoor human activity and trajectory tracking. However, its performance is fundamentally limited by Non-Line-of-Sight (NLOS) errors and kinematic drift during turns. To address these challenges, this study introduces a novel integrated IMU-UWB framework for walking trajectory estimation in NLOS scenarios involving turning gait. The algorithm integrates an error-state Kalman filter (ESKF) and a phase-aware turning correction module. Experiments were carried out to evaluate the effectiveness of this framework. The results show that the presented framework demonstrates significant improvements in walking trajectory estimation, with a smaller mean absolute error (7.0 cm) and a higher correlation coefficient, compared to the traditional methods. By effectively mitigating both NLOS-induced ranging errors and turn-related drift, this system enables reliable indoor tracking for healthcare monitoring, industrial safety, and consumer navigation applications. Full article
(This article belongs to the Special Issue Recent Advance of Auto Navigation in Indoor Scenarios)
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22 pages, 3641 KB  
Article
Research on the Localization Method of Ground Electrode Current Field Signal Based on Fractional Fourier Transform
by Sirui Chu, Hui Zhao, Zhong Su, Xiangxian Yao, Yanke Wang, Zhongao Ling and Xibing Gu
Electronics 2025, 14(17), 3380; https://doi.org/10.3390/electronics14173380 - 25 Aug 2025
Cited by 2 | Viewed by 1435
Abstract
Aiming at the problem of a lack of positioning satellites and no available beacons in underground space, an injected ground electrode current field signal localization method is proposed. An extremely low-frequency current field signal is applied to two pairs of electrodes inserted into [...] Read more.
Aiming at the problem of a lack of positioning satellites and no available beacons in underground space, an injected ground electrode current field signal localization method is proposed. An extremely low-frequency current field signal is applied to two pairs of electrodes inserted into the earth to form a ground current field underground, and the ground electrode current field signal detected at the detection end is used for localization, which can effectively provide reference localization for the underground space when the satellite positioning fails. On this basis, considering that the ground electrode current field signal is susceptible to the influence of the geological structure, electromagnetic interference, and the complexity of the propagation path during underground transmission, which results in the signal showing strong non-stationary characteristics, it is difficult for the traditional time–frequency analysis method to accurately extract stable and reliable positioning characteristics. In order to improve the signal-processing accuracy and robustness, this paper introduces fractional Fourier transform (FRFT) to process the detected signals, and focuses the signal energy more effectively under the optimal order. In order to verify the effectiveness of the localization method, several experiments on the localization of ground electrode current field signals are carried out in the underground space. The experimental results show that, in the positioning environment of more than 10,000 square meters, the average positioning error is 6.896 m. The application of this method will provide a solid technical support for life rescue in underground space, provide the ‘last protection’ for rescue, and complete the life chain of emergency first aid, which has an important application prospect and practical value. Full article
(This article belongs to the Special Issue Recent Advance of Auto Navigation in Indoor Scenarios)
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21 pages, 5206 KB  
Article
Innovative Indoor Positioning: BLE Beacons for Healthcare Tracking
by Erika Skýpalová, Martin Boroš, Tomáš Loveček and Andrej Veľas
Electronics 2025, 14(10), 2018; https://doi.org/10.3390/electronics14102018 - 15 May 2025
Cited by 3 | Viewed by 8668
Abstract
Indoor localization systems are gaining increasing relevance due to the limitations of traditional Global Positioning System (GPS) technology in enclosed environments. While the GPS remains widely used for navigation, its efficacy is significantly reduced indoors or in confined spaces. Given the growing societal [...] Read more.
Indoor localization systems are gaining increasing relevance due to the limitations of traditional Global Positioning System (GPS) technology in enclosed environments. While the GPS remains widely used for navigation, its efficacy is significantly reduced indoors or in confined spaces. Given the growing societal and technological demand for precise localization and movement tracking within such environments, the development of indoor positioning systems (IPSs) has become a critical area of research. Among the available technologies, Bluetooth Low Energy (BLE) beacons have emerged as one of the most promising solutions for indoor positioning applications. This paper presents an indoor positioning system leveraging BLE beacons, specifically designed for deployment in confined environments. The system employed the Fingerprinting method for localization, and its prototype was experimentally tested within a selected healthcare facility. A series of systematic tests confirmed both the functional reliability of the proposed system and its capability to provide precise localization tailored to the spatial characteristics of the given environment. This research offers a novel application of BLE beacon technology, as it extends beyond simple presence detection to enable accurate position determination at defined time intervals and the relative positioning of multiple entities within the monitored space. Full article
(This article belongs to the Special Issue Recent Advance of Auto Navigation in Indoor Scenarios)
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24 pages, 26629 KB  
Article
Optimization Model-Based Robust Method and Performance Evaluation of GNSS/INS Integrated Navigation for Urban Scenes
by Dashuai Chai, Shijie Song, Kunlin Wang, Jingxue Bi, Yunlong Zhang, Yipeng Ning and Ruijie Yan
Electronics 2025, 14(4), 660; https://doi.org/10.3390/electronics14040660 - 8 Feb 2025
Cited by 4 | Viewed by 1943
Abstract
The robust and high-precision estimation of position and attitude information using a combined global navigation satellite system/inertial navigation system (GNSS/INS) model is essential to a wide range of applications in intelligent driving and smart transportation. GNSS systems are susceptible to inaccuracies and signal [...] Read more.
The robust and high-precision estimation of position and attitude information using a combined global navigation satellite system/inertial navigation system (GNSS/INS) model is essential to a wide range of applications in intelligent driving and smart transportation. GNSS systems are susceptible to inaccuracies and signal interruptions in occluded environments, which lead to unreliable parameter estimations in GNSS/INS based on filter models. To address this issue, in this paper, a GNSS/INS combination model based on factor graph optimization (FGO) is investigated and the robustness of this optimization model is evaluated in comparison to the traditional extended Kalman filter (EKF) model and robust Kalman filter (RKF) model. In this paper, both high- and low-accuracy GNSS/INS combination data are used and the two sets of urban scene data are collected using high- and low-precision consumer-grade inertial guidance systems and an in-vehicle setup. The experimental results demonstrate that the position, velocity, and attitude estimates obtained using the GNSS/INS and the FGO model are superior to those obtained using the traditional EKF and robust EKF methods. In the simulated scenarios involving gross interference and GNSS signal loss, the FGO model achieves optimal results. The maximum improvement rates of the position, velocity, and attitude estimates are 81.1%, 73.8%, and 75.1% compared to the EKF method and 79.8%, 72.1%, and 57.1% compared to the RKF method, respectively. Full article
(This article belongs to the Special Issue Recent Advance of Auto Navigation in Indoor Scenarios)
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