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UAV and Sensors Applications for Navigation and Positioning

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 10 April 2025 | Viewed by 21969

Special Issue Editors


E-Mail Website
Guest Editor
School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
Interests: sensors applications for navigation and positioning; filtering and information fusion; structural safety and reliability; intelligent aircraft technology

E-Mail Website
Guest Editor
Institute of Solid Mechanics, Beihang University (BUAA), Beijing 100191, China
Interests: fabrication of wearable electronics and flexible electronics; thermal management of flexible electronics; mechanics design of flexieble electronics

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAV) are widely used, from military to civilian and commercial, such as military reconnaissance, power inspection, security inspection and other special tasks. However, the prerequisite for UAV to complete the task is to achieve high-precision navigation and positioning, that is, the UAV should know where it is in real time. Global navigation satellite system (GNSS) technology is the mainstream navigation and positioning method for UAV in open areas; while in GNSS-denied environments such as dense forests, mountains and indoors, it becomes a challenge to realize high-precision navigation and positioning of UAVs.

Benefiting from the development of microelectro mechanical systems (MEMS), a variety of sensors can be carried on UAVs, and the navigation and positioning technology based on the fusion of multi-sensor data has become a research hotspot. The vigorous development of cluster technology, machine learning, simultaneous localization and mapping (SLAM) brings more possibilities for UAV navigation and positioning methods. Researchers are making innovative achievements in these areas, such as SLAM, cooperative navigation and positioning for multi-UAV, the applications of machine learning and so on.

This Special Issue aims to raise innovative papers related to “UAV and Sensors Applications for Navigation and Positioning”, including but not limited to:

  • Navigation and positioning method in GNSS-denied environment
  • Multi-sensor data fusion method for UAV navigation and positioning
  • Cooperative navigation and positioning method for multi-UAV
  • SLAM
  • Vision-based navigation and positioning method
  • Applications of machine learning in UAV navigation and positioning

Dr. Yongbo Zhang
Prof. Dr. Yuhang Li
Guest Editors

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Keywords

  • unmanned aerial vehicles 
  • sensors applications 
  • navigation and positioning 
  • SLAM 
  • machine learning 
  • data fusion

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Published Papers (11 papers)

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Research

18 pages, 4227 KiB  
Article
A Novel Multi-Objective Trajectory Planning Method for Robots Based on the Multi-Objective Particle Swarm Optimization Algorithm
by Jiahui Wang, Yongbo Zhang, Shihao Zhu and Junling Wang
Sensors 2024, 24(23), 7663; https://doi.org/10.3390/s24237663 - 29 Nov 2024
Viewed by 345
Abstract
The three performance indexes of the space robot, travel time, energy consumption, and smoothness, are the key to its important role in space exploration. Therefore, this paper proposes a multi-objective trajectory planning method for robots. Firstly, the kinematics and dynamics of the Puma560 [...] Read more.
The three performance indexes of the space robot, travel time, energy consumption, and smoothness, are the key to its important role in space exploration. Therefore, this paper proposes a multi-objective trajectory planning method for robots. Firstly, the kinematics and dynamics of the Puma560 robot are analyzed to lay the foundation for trajectory planning. Secondly, the joint space trajectory of the robot is constructed with fifth-order B-spline functions, realizing the continuous position, velocity, acceleration, and jerk of each joint. Then, the improved multi-objective particle swarm optimization (MOPSO) algorithm is used to optimize the trajectory, and the distribution uniformity, convergence, and diversity of the obtained Pareto front are good. The improved MOPSO algorithm can realize the optimization between multiple objectives and obtain the trajectory that meets the actual engineering requirements. Finally, this paper implements the visualization of the robot’s joints moving according to the optimal trajectory. Full article
(This article belongs to the Special Issue UAV and Sensors Applications for Navigation and Positioning)
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24 pages, 8598 KiB  
Article
Differential Positioning with Bluetooth Low Energy (BLE) Beacons for UAS Indoor Operations: Analysis and Results
by Salvatore Ponte, Gennaro Ariante, Alberto Greco and Giuseppe Del Core
Sensors 2024, 24(22), 7170; https://doi.org/10.3390/s24227170 - 8 Nov 2024
Viewed by 778
Abstract
Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time [...] Read more.
Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the RSSI (received signal strength indicator) for distance estimation and positioning. Distance information from measured RSSI values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the RSSI and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the RSSI and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique. Full article
(This article belongs to the Special Issue UAV and Sensors Applications for Navigation and Positioning)
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13 pages, 1417 KiB  
Article
Deep Compressed Communication and Application in Multi-Robot 2D-Lidar SLAM: An Intelligent Huffman Algorithm
by Liang Zhang and Jinghui Deng
Sensors 2024, 24(10), 3154; https://doi.org/10.3390/s24103154 - 16 May 2024
Viewed by 1455
Abstract
Multi-robot Simultaneous Localization and Mapping (SLAM) systems employing 2D lidar scans are effective for exploration and navigation within GNSS-limited environments. However, scalability concerns arise with larger environments and increased robot numbers, as 2D mapping necessitates substantial processor memory and inter-robot communication bandwidth. Thus, [...] Read more.
Multi-robot Simultaneous Localization and Mapping (SLAM) systems employing 2D lidar scans are effective for exploration and navigation within GNSS-limited environments. However, scalability concerns arise with larger environments and increased robot numbers, as 2D mapping necessitates substantial processor memory and inter-robot communication bandwidth. Thus, data compression prior to transmission becomes imperative. This study investigates the problem of communication-efficient multi-robot SLAM based on 2D maps and introduces an architecture that enables compressed communication, facilitating the transmission of full maps with significantly reduced bandwidth. We propose a framework employing a lightweight feature extraction Convolutional Neural Network (CNN) for a full map, followed by an encoder combining Huffman and Run-Length Encoding (RLE) algorithms to further compress a full map. Subsequently, a lightweight recovery CNN was designed to restore map features. Experimental validation involves applying our compressed communication framework to a two-robot SLAM system. The results demonstrate that our approach reduces communication overhead by 99% while maintaining map quality. This compressed communication strategy effectively addresses bandwidth constraints in multi-robot SLAM scenarios, offering a practical solution for collaborative SLAM applications. Full article
(This article belongs to the Special Issue UAV and Sensors Applications for Navigation and Positioning)
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20 pages, 13527 KiB  
Article
Attention Mechanism and LSTM Network for Fingerprint-Based Indoor Location System
by Zhen Wu, Peng Hu, Shuangyue Liu and Tao Pang
Sensors 2024, 24(5), 1398; https://doi.org/10.3390/s24051398 - 21 Feb 2024
Cited by 2 | Viewed by 1685
Abstract
The demand for precise indoor localization services is steadily increasing. Among various methods, fingerprint-based indoor localization has become a popular choice due to its exceptional accuracy, cost-effectiveness, and ease of implementation. However, its performance degrades significantly as a result of multipath signal attenuation [...] Read more.
The demand for precise indoor localization services is steadily increasing. Among various methods, fingerprint-based indoor localization has become a popular choice due to its exceptional accuracy, cost-effectiveness, and ease of implementation. However, its performance degrades significantly as a result of multipath signal attenuation and environmental changes. In this paper, we propose an indoor localization method based on fingerprints using self-attention and long short-term memory (LSTM). By integrating a self-attention mechanism and LSTM network, the proposed method exhibits outstanding positioning accuracy and robustness in diverse experimental environments. The performance of the proposed method is evaluated under two different experimental scenarios, which involve 2D and 3D moving trajectories, respectively. The experimental results demonstrate that our approach achieves an average localization error of 1.76 m and 2.83 m in the respective scenarios, outperforming the existing state-of-the-art methods by 42.67% and 31.64%. Full article
(This article belongs to the Special Issue UAV and Sensors Applications for Navigation and Positioning)
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11 pages, 1299 KiB  
Article
SiaN-VO: Siamese Network for Visual Odometry
by Bruno S. Faiçal, Cesar A. C. Marcondes and Filipe A. N. Verri
Sensors 2024, 24(3), 973; https://doi.org/10.3390/s24030973 - 2 Feb 2024
Cited by 1 | Viewed by 1037
Abstract
Despite the significant advancements in drone sensory device reliability, data integrity from these devices remains critical in securing successful flight plans. A notable issue is the vulnerability of GNSS to jamming attacks or signal loss from satellites, potentially leading to incomplete drone flight [...] Read more.
Despite the significant advancements in drone sensory device reliability, data integrity from these devices remains critical in securing successful flight plans. A notable issue is the vulnerability of GNSS to jamming attacks or signal loss from satellites, potentially leading to incomplete drone flight plans. To address this, we introduce SiaN-VO, a Siamese neural network designed for visual odometry prediction in such challenging scenarios. Our preliminary studies have shown promising results, particularly for flights under static conditions (constant speed and altitude); while these findings are encouraging, they do not fully represent the complexities of real-world flight conditions. Therefore, in this paper, we have furthered our research to enhance SiaN-VO, improving data integration from multiple sensors and enabling more accurate displacement predictions in dynamic flight conditions, thereby marking a significant step forward in drone navigation technology. Full article
(This article belongs to the Special Issue UAV and Sensors Applications for Navigation and Positioning)
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21 pages, 13373 KiB  
Article
Research on a Method of Locating Civil Aviation Radio Interference Sources Based on Time Difference of Arrival and Frequency Difference of Arrival for Four Unmanned Aerial Vehicles
by Chao Zhou, Xingyu Zhu, Renhe Xiong, Kun Hu, Feng Ouyang, Chi Huang and Tao Huang
Sensors 2023, 23(18), 7939; https://doi.org/10.3390/s23187939 - 16 Sep 2023
Viewed by 1385
Abstract
Monitoring and analyzing radio interference sources play a crucial role in ensuring the safe operation of civil aviation navigation, communication, airport management, and air traffic control. Traditional ground monitoring methods are slow and inadequate for tracking aerial and mobile interference sources effectively. Although [...] Read more.
Monitoring and analyzing radio interference sources play a crucial role in ensuring the safe operation of civil aviation navigation, communication, airport management, and air traffic control. Traditional ground monitoring methods are slow and inadequate for tracking aerial and mobile interference sources effectively. Although flight methods such as helicopters and airships can effectively monitor aerial interference, the flight approval process is time-consuming and expensive. This paper investigates a novel approach to locating civil aviation radio interference sources using four unmanned aerial vehicles (UAVs) to address this issue. It establishes a model for aerial positioning of radio interference sources with the four UAVs and proposes a method for time synchronization and data communication among them. The paper conducts simulations of the four-UAV time–frequency difference positioning method, analyzing the geometric accuracy dilution with different deployment configurations of the UAVs, positioning biases, and root mean square errors (RMSEs) under varying interference source movement speeds. The simulation results provide crucial data to support subsequent experiments. Full article
(This article belongs to the Special Issue UAV and Sensors Applications for Navigation and Positioning)
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26 pages, 8737 KiB  
Article
Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process
by Abhishek Phadke, F. Antonio Medrano, Chandra N. Sekharan and Tianxing Chu
Sensors 2023, 23(17), 7359; https://doi.org/10.3390/s23177359 - 23 Aug 2023
Cited by 12 | Viewed by 5468
Abstract
The rapid advancement and increasing number of applications of Unmanned Aerial Vehicle (UAV) swarm systems have garnered significant attention in recent years. These systems offer a multitude of uses and demonstrate great potential in diverse fields, ranging from surveillance and reconnaissance to search [...] Read more.
The rapid advancement and increasing number of applications of Unmanned Aerial Vehicle (UAV) swarm systems have garnered significant attention in recent years. These systems offer a multitude of uses and demonstrate great potential in diverse fields, ranging from surveillance and reconnaissance to search and rescue operations. However, the deployment of UAV swarms in dynamic environments necessitates the development of robust experimental designs to ensure their reliability and effectiveness. This study describes the crucial requirement for comprehensive experimental design of UAV swarm systems before their deployment in real-world scenarios. To achieve this, we begin with a concise review of existing simulation platforms, assessing their suitability for various specific needs. Through this evaluation, we identify the most appropriate tools to facilitate one’s research objectives. Subsequently, we present an experimental design process tailored for validating the resilience and performance of UAV swarm systems for accomplishing the desired objectives. Furthermore, we explore strategies to simulate various scenarios and challenges that the swarm may encounter in dynamic environments, ensuring comprehensive testing and analysis. Complex multimodal experiments may require system designs that may not be completely satisfied by a single simulation platform; thus, interoperability between simulation platforms is also examined. Overall, this paper serves as a comprehensive guide for designing swarm experiments, enabling the advancement and optimization of UAV swarm systems through validation in simulated controlled environments. Full article
(This article belongs to the Special Issue UAV and Sensors Applications for Navigation and Positioning)
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20 pages, 3254 KiB  
Article
FCCD-SAR: A Lightweight SAR ATR Algorithm Based on FasterNet
by Xiang Dong, Dong Li and Jiandong Fang
Sensors 2023, 23(15), 6956; https://doi.org/10.3390/s23156956 - 5 Aug 2023
Cited by 4 | Viewed by 2161
Abstract
In recent times, the realm of remote sensing has witnessed a remarkable surge in the area of deep learning, specifically in the domain of target recognition within synthetic aperture radar (SAR) images. However, prevailing deep learning models have often placed undue emphasis on [...] Read more.
In recent times, the realm of remote sensing has witnessed a remarkable surge in the area of deep learning, specifically in the domain of target recognition within synthetic aperture radar (SAR) images. However, prevailing deep learning models have often placed undue emphasis on network depth and width while disregarding the imperative requirement for a harmonious equilibrium between accuracy and speed. To address this concern, this paper presents FCCD-SAR, a SAR target recognition algorithm based on the lightweight FasterNet network. Initially, a lightweight and SAR-specific feature extraction backbone is meticulously crafted to better align with SAR image data. Subsequently, an agile upsampling operator named CARAFE is introduced, augmenting the extraction of scattering information and fortifying target recognition precision. Moreover, the inclusion of a rapid, lightweight module, denoted as C3-Faster, serves to heighten both recognition accuracy and computational efficiency. Finally, in cognizance of the diverse scales and vast variations exhibited by SAR targets, a detection head employing DyHead’s attention mechanism is implemented to adeptly capture feature information across multiple scales, elevating recognition performance on SAR targets. Exhaustive experimentation on the MSTAR dataset unequivocally demonstrates the exceptional prowess of our FCCD-SAR algorithm, boasting a mere 2.72 M parameters and 6.11 G FLOPs, culminating in an awe-inspiring 99.5% mean Average Precision (mAP) and epitomizing its unparalleled proficiency. Full article
(This article belongs to the Special Issue UAV and Sensors Applications for Navigation and Positioning)
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18 pages, 7120 KiB  
Article
IMU/UWB Fusion Method Using a Complementary Filter and a Kalman Filter for Hybrid Upper Limb Motion Estimation
by Yutong Shi, Yongbo Zhang, Zhonghan Li, Shangwu Yuan and Shihao Zhu
Sensors 2023, 23(15), 6700; https://doi.org/10.3390/s23156700 - 26 Jul 2023
Cited by 9 | Viewed by 2626
Abstract
Motion capture systems have enormously benefited the research into human–computer interaction in the aerospace field. Given the high cost and susceptibility to lighting conditions of optical motion capture systems, as well as considering the drift in IMU sensors, this paper utilizes a fusion [...] Read more.
Motion capture systems have enormously benefited the research into human–computer interaction in the aerospace field. Given the high cost and susceptibility to lighting conditions of optical motion capture systems, as well as considering the drift in IMU sensors, this paper utilizes a fusion approach with low-cost wearable sensors for hybrid upper limb motion tracking. We propose a novel algorithm that combines the fourth-order Runge–Kutta (RK4) Madgwick complementary orientation filter and the Kalman filter for motion estimation through the data fusion of an inertial measurement unit (IMU) and an ultrawideband (UWB). The Madgwick RK4 orientation filter is used to compensate gyroscope drift through the optimal fusion of a magnetic, angular rate, and gravity (MARG) system, without requiring knowledge of noise distribution for implementation. Then, considering the error distribution provided by the UWB system, we employ a Kalman filter to estimate and fuse the UWB measurements to further reduce the drift error. Adopting the cube distribution of four anchors, the drift-free position obtained by the UWB localization Kalman filter is used to fuse the position calculated by IMU. The proposed algorithm has been tested by various movements and has demonstrated an average decrease in the RMSE of 1.2 cm from the IMU method to IMU/UWB fusion method. The experimental results represent the high feasibility and stability of our proposed algorithm for accurately tracking the movements of human upper limbs. Full article
(This article belongs to the Special Issue UAV and Sensors Applications for Navigation and Positioning)
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23 pages, 9678 KiB  
Article
A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied Environments
by Jia Cheng, Peng Ren and Tingxiang Deng
Sensors 2023, 23(9), 4366; https://doi.org/10.3390/s23094366 - 28 Apr 2023
Cited by 6 | Viewed by 2203
Abstract
Global Navigation Satellite Systems (GNSS) with weak anti-jamming capability are vulnerable to intentional or unintentional interference, resulting in difficulty providing continuous, reliable, and accurate positioning information in complex environments. Especially in GNSS-denied environments, relying solely on the onboard Inertial Measurement Unit (IMU) of [...] Read more.
Global Navigation Satellite Systems (GNSS) with weak anti-jamming capability are vulnerable to intentional or unintentional interference, resulting in difficulty providing continuous, reliable, and accurate positioning information in complex environments. Especially in GNSS-denied environments, relying solely on the onboard Inertial Measurement Unit (IMU) of the Micro Aerial Vehicles (MAVs) for positioning is not practical. In this paper, we propose a novel cooperative relative positioning method for MAVs in GNSS-denied scenarios. Specifically, the system model framework is first constructed, and then the Extended Kalman Filter (EKF) algorithm, which is introduced for its ability to handle nonlinear systems, is employed to fuse inter-vehicle ranging and onboard IMU information, achieving joint position estimation of the MAVs. The proposed method mainly addresses the problem of error accumulation in the IMU and exhibits high accuracy and robustness. Additionally, the method is capable of achieving relative positioning without requiring an accurate reference anchor. The system observability conditions are theoretically derived, which means the system positioning accuracy can be guaranteed when the system satisfies the observability conditions. The results further demonstrate the validity of the system observability conditions and investigate the impact of varying ranging errors on the positioning accuracy and stability. The proposed method achieves a positioning accuracy of approximately 0.55 m, which is about 3.89 times higher than that of an existing positioning method. Full article
(This article belongs to the Special Issue UAV and Sensors Applications for Navigation and Positioning)
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17 pages, 984 KiB  
Article
Scheduling Framework for Accelerating Multiple Detection-Free Object Trackers
by Myungsun Kim, Inmo Kim, Jihyeon Yong and Hyuksoo Kim
Sensors 2023, 23(7), 3432; https://doi.org/10.3390/s23073432 - 24 Mar 2023
Cited by 1 | Viewed by 1461
Abstract
In detection-free tracking, after users freely designate the location of the object to be tracked in the first frame of the video sequence, the location of the object is continuously found in the following video frame sequence. Recently, technologies using a Siamese network [...] Read more.
In detection-free tracking, after users freely designate the location of the object to be tracked in the first frame of the video sequence, the location of the object is continuously found in the following video frame sequence. Recently, technologies using a Siamese network and transformer based on DNN modules have been evaluated as very excellent in terms of tracking accuracy. The high computational complexity due to the usage of the DNN module is not a preferred feature in terms of execution speed, and when tracking two or more objects, a bottleneck effect occurs in the DNN accelerator such as the GPU, which inevitably results in a larger delay. To address this problem, we propose a tracker scheduling framework. First, the computation structures of representative trackers are analyzed, and the scheduling unit suitable for the execution characteristics of each tracker is derived. Based on this analysis, the decomposed workloads of trackers are multi-threaded under the control of the scheduling framework. CPU-side multi-threading leads the GPU to a work-conserving state while enabling parallel processing as much as possible even within a single GPU depending on the resource availability of the internal hardware. The proposed framework is a general-purpose system-level software solution that can be applied not only to GPUs but also to other hardware accelerators. As a result of confirmation through various experiments, when tracking two objects, the execution speed was improved by up to 55% while maintaining almost the same accuracy as the existing method. Full article
(This article belongs to the Special Issue UAV and Sensors Applications for Navigation and Positioning)
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