Search Results (71)

The increasing demand for efficient aviation navigation system inspections has led to the use of Unmanned Aerial Vehicles (UAVs) as a flexible and cost-effective alternative to traditional manned aircraft. This study emphasizes the operational advantages of UAVs in transforming flight inspections, including Non-Directional Beacon (NDB) calibration. Following the successful performance evaluation of an NDB system in Banská Bystrica, Slovakia, using a manned aircraft, a UAV was deployed on the same flight path to validate its ability to replicate the procedure in terms of trajectory only, without performing any signal measurement. The UAV maintained accurate flight paths and continuous communication throughout the mission. A specialized rotatory system, operating at 868 MHz, enabled real-time tracking and ensured stable communication over long distances. The manned aircraft test revealed a maximum bearing deviation of 13.47° at 3.37 NM and a minimum received signal strength of −90 dBm, which approaches the ICAO threshold for en route navigation (±10°) but remains usable for diagnostic purposes. The UAV flight did not include signal capture but successfully completed the 40 NM profile with a circular error probable (CEP95) of 2.8 m and communication link uptime of 99.8%, confirming that the vehicle can meet procedural trajectory fidelity. These findings support the feasibility of UAV-based NDB inspections and provide the foundation for future test phases with onboard signal monitoring systems. Full article

This paper presents a hybrid indoor localization framework combining time difference of arrival (TDoA) measurements with a swarm intelligence optimization technique. To address the nonlinear optimization challenges in three-dimensional (3D) indoor localization via TDoA measurements, we systematically evaluate the artificial bee colony (ABC) algorithm and chimpanzee optimization algorithm (ChOA). Through comprehensive Monte Carlo simulations in a cubic 3D environment with eight beacons, our comparative analysis reveals that the ChOA achieves superior localization accuracy while maintaining computational efficiency. Building upon the ChOA framework, we introduce a multi-beacon fusion strategy incorporating a local outlier factor-based linear weighting mechanism to enhance robustness against measurement noise and improve localization accuracy. This approach integrates spatial density estimation with geometrically consistent weighting of distributed beacons, effectively filtering measurement outliers through adaptive sensor fusion. The experimental results show that the proposed algorithm exhibits excellent convergence performance under the condition of a low population size. Its anti-interference capability against Gaussian white noise is significantly improved compared with the baseline algorithms, and its anti-interference performance against multipath noise is consistent with that of the baseline algorithms. However, in terms of dealing with UWB device failures, the performance of the algorithm is slightly inferior. Meanwhile, the algorithm has relatively good time-lag performance and target-tracking performance. The study provides theoretical insights and practical guidelines for deploying reliable localization systems in complex indoor environments. Full article

The operational integrity of the BeiDou-3 Navigation Satellite System (BDS-3) has been significantly challenged by electromagnetic interference, particularly from Distance Measuring Equipment (DME) ground beacons to the newly implemented B2a signal, since its full operational deployment in 2020. This study developed a comprehensive interference evaluation model based on receiver signal processing principles to quantify the degradation of B2a signal reception performance under DME interference scenarios. Leveraging empirical data from the DME beacon network in the Chinese mainland, we systematically analyzed the interference effects through an effective carrier-to-noise ratio ($C/N_0$), signal detection probability, carrier tracking accuracy, and demodulation bit error rate (BER). The results demonstrate that the effective $C/N_0$ of the B2a signal degrades by up to 3.25 dB, the detection probability decreases by 33%, and the carrier tracking errors and BER increase by 2.57° and 5.1%, respectively, in worst-case interference scenarios. Furthermore, significant spatial correlation was observed between the interference hotspots and regions of high aircraft density. DME interference adversely affected the accuracy, availability, continuity, and integrity of the airborne BeiDou navigation system, thereby compromising civil aviation flight safety. These findings establish a scientific foundation for developing Minimum Operational Performance Standards for B2a signal receivers and for strategically optimizing DME beacon deployment throughout the Chinese mainland. Full article

The linear pseudo-measurements filter is adapted for use in a stochastic observation system with random time delays between the arrival of observations and the actual state of a moving object. The observation model is characterized by limited prior knowledge of the measurement errors distribution, specified only by its first two moments. Furthermore, the proposed model allows for a multiplicative dependence of errors on the state of the moving object. The filter incorporates direction angles and range measurements generated by several independent measurement complexes. As a practical application, the method is used for tracking an autonomous underwater vehicle moving toward a stationary target. The vehicle’s velocity is influenced by continuous random disturbances and periodic abrupt changes. Observations are performed by two stationary acoustic beacons. Full article

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

Occupancy monitoring (OM) and the localization of individuals within indoor environments using wearable devices offer a very promising data communication solution in applications such as home automation, smart office management, outbreak monitoring, and emergency operating plans. OM is challenging when developing solutions that focus on reduced power consumption and cost. Bluetooth low energy (BLE) technology is energy- and cost-efficient compared to other technologies. Integrating BLE Received Signal Strength Indicator (RSSI) signals with machine learning (ML) introduces a new Artificial Intelligence- (AI-) enhanced OM approach. In this paper, we propose an Intelligent Bluetooth Virtual Door (IBVD) OM system for the indoor/outdoor tracking of individuals using the interaction between a BLE device worn by the occupant and two BLE beacons located at the entrance/exit points of a doorway. ML algorithms are used to perform intelligent OM through pattern detection from the BLE RSSI signal(s). This approach differs from other technologies in that it does not require any floorplan information. The developed OM system achieves a range between 96.6% and 97.3% classification accuracy for all tested ML models, where the error translates to a minor delay in the time in which an individual’s location is classified, introducing a highly reliable indoor/outdoor tracking system. Full article

High-precision indoor localization and tracking are essential requirements for the safe navigation and task execution of autonomous mobile robots. Despite the growing importance of mobile robots in various areas, achieving precise indoor localization remains challenging due to signal interference, multipath propagation, and complex indoor layouts. In this work, we present the first comprehensive study comparing the accuracy of Bluetooth low energy (BLE), WiFi, and ultra wideband (UWB) technologies for the indoor localization of mobile robots under various circumstances. In the performed experiments, the error margin of the WiFi-based systems reached 608.7 cm, which is not tolerable for most applications. As a commonly used technology in the existing tracking systems, the accuracy of BLE-based systems is at least 44.12% better than that of WiFi-based systems. The error margin of the BLE-based system in tracking static and mobile robots was 191.7 cm and 340.1 cm, respectively. The experiments showed that even with a limited number of UWB anchors, the system provides acceptable accuracy for tracking the mobile robots. Using only four UWB beacons in an environment of about 431 m² area, the maximum error margin of detected positions by the UWB-based tracking system remained below 13.1 cm and 28.9 cm on average for the static and mobile robots, respectively. This error margin is 88.05% lower than that of the BLE-based system and 93.27% lower than that of the WiFi-based system on average. The high tracking precision, the need for a lower number of anchors, and the decreasing hardware costs point out that UWB will be the dominating technology in indoor tracking systems in the near future. Full article

This paper proposes an innovative, intelligent shopping cart system with an interdisciplinary approach using Bluetooth low energy (BLE) beacons. The research integrates online and offline retail strategies by presenting campaigns and ads to the customers during in-store navigation. In a testing environment, BLE beacons are strategically positioned to monitor the purchasing process and deliver relevant insights to retailers. The technology anonymously logs customers’ locations and the duration of their browsing at each sales shelf. Through the analysis of client movement heatmaps, retailers may discern high-traffic zones and modify product placement to enhance visibility and sales. Additionally, the system provides an additional revenue model for store owners through location specific targeted ads displayed on a tablet mounted on the cart. Unlike previous BLE-based tracking solutions, this research bridges the gap between customer movement analytics and real-time targeted advertising in retail settings. The system achieved an accuracy of 82.4% when the aisle partition length was 3.00 m and 91.7% when the aisle partition length was 6.00 m. This system, which can generate additional income for store owners by generating 0.171 USD in a single test simulation as a result of displaying ads to three test customers in a two-partitioned aisle layout, offers a new and scalable business model for modern retailers. Full article

Vehicle-to-everything (V2X) communication is pivotal in enhancing cooperative awareness in vehicular networks. Typically, awareness is viewed as a vehicle’s ability to perceive and share real-time kinematic information. We present a novel definition of awareness in V2X communications, conceptualizing it as a multi-faceted concept involving vehicle detection, tracking, and maintaining their safety distances. To enhance this awareness, we propose a deep reinforcement learning framework for the joint control of beacon rate and transmit power (DRL-JCBRTP). Our DRL−JCBRTP framework integrates LSTM-based actor networks and MLP-based critic networks within the Soft Actor-Critic (SAC) algorithm to effectively learn optimal policies. Leveraging local state information, the DRL-JCBRTP scheme uses an innovative reward function to increase the minimum awareness failure distance. Our SLMLab-Gym-VEINS simulations show that the DRL-JCBRTP scheme outperforms existing beaconing schemes in minimizing awareness failure probability and maximizing awareness distance, ultimately improving driving safety. Full article

Context: In Search and Rescue (SAR) operations, the speed and techniques used by rescuers and effective communication with the person in need of rescue are vital for successful operations. Recently, drones have become an essential tool in SAR, used by both military and civilian organizations to locate and aid missing persons. Objective: The paper introduces Transponder, a Wi-Fi-based solution designed to enhance SAR efforts by tracking, localizing, and providing first aid information to distressed individuals, even in challenging environments such as forests, mountains, and urban areas lacking GSM/UMTS coverage or that are difficult to reach with terrestrial rescue. Methods: Provide an innovative mechanism based on Wi-Fi beacon detection, LoRa communication, and the possible mobile application to leverage the SAR operation. Provide the preliminary implementation of the Transponder and perform its assessment in scenarios with dense vegetation. Results: The Transponder functionalities have been proven to enhance and expedite the detection of missing persons. Additionally, responses to several research questions regarding its performance and effectiveness are provided. Conclusions: Transponder is an innovative detection mechanism that combines ground-based analysis with on-board analysis, optimizing energy consumption and realizing an efficient solution for real-world scenarios. Full article

UWB has been in existence for several years, but it was only a few years ago that it transitioned from a specialized niche to more mainstream applications. Recent market data indicate a rapid increase in the popularity of UWB in consumer products, such as smartphones and smart home devices, as well as automotive and industrial real-time location systems. The challenge of achieving accurate positioning in indoor environments arises from various factors such as distance, location, beacon density, dynamic surroundings, and the density and type of obstacles. This research used MFi-certified UWB beacon chipsets and integrated them with a mobile application dedicated to iOS by implementing the near interaction accessory protocol. The analysis covers both static and dynamic cases. Thanks to the acquisition of measurements, two main candidates for indoor localization infrastructure were analyzed and compared in terms of accuracy, namely UWB and LIDAR, with the latter used as a reference system. The problem of achieving accurate positioning in various applications and environments was analyzed, and future solutions were proposed. The results show that the achieved accuracy is sufficient for tracking individuals and may serve as guidelines for achievable accuracy or may provide a basis for further research into a complex sensor fusion-based navigation system. This research provides several findings. Firstly, in dynamic conditions, LIDAR measurements showed higher accuracy than UWB beacons. Secondly, integrating data from multiple sensors could enhance localization accuracy in non-line-of-sight scenarios. Lastly, advancements in UWB technology may expand the availability of competitive hardware, facilitating a thorough evaluation of its accuracy and effectiveness in practical systems. These insights may be particularly useful in designing navigation systems for blind individuals in buildings. Full article

Inefficient patient transport in hospitals often leads to delays, overworked staff, and suboptimal resource utilization, ultimately impacting patient care. Existing dispatch management algorithms are often evaluated in simulation environments, raising concerns about their real-world applicability. This study presents a real-world experiment that bridges the gap between theoretical dispatch algorithms and real-world implementation. It applies process capability analysis at Taichung Veterans General Hospital in Taichung, Taiwan, and utilizes IoT for real-time tracking of staff and medical devices to address challenges associated with manual dispatch processes. Experimental data collected from the hospital underwent statistical evaluation between January 2021 and December 2021. The results of our experiment, which compared the use of traditional dispatch methods with the Beacon dispatch method, found that traditional dispatch had an overtime delay of 41.0%; in comparison, the Beacon dispatch method had an overtime delay of 26.5%. These findings demonstrate the transformative potential of this solution for not only hospital operations but also for improving service quality across the healthcare industry in the context of smart hospitals. Full article

In this paper, we present a novel non-deep-learning-based approach for real-time object tracking and activity recognition within smart homes, aiming to minimize human intervention and dataset requirements. Our method utilizes discreet, easily concealable sensors and passive RFID technology to track objects in real-time, enabling precise activity recognition without the need for extensive datasets typically associated with deep learning techniques. Central to our approach is AtomGID, an algorithm tailored to extract highly generalizable spatial features from RFID data. Notably, AtomGID’s adaptability extends beyond RFID to other imprecise tracking technologies like Bluetooth beacons and radars. We validate AtomGID through simulation and real-world RFID data collection within a functioning smart home environment. To enhance recognition accuracy, we employ a clustering adaptation of the flocking algorithm, leveraging previously published Activities of Daily Living (ADLs) data. Our classifier achieves a robust classification rate ranging from 85% to 93%, underscoring the efficacy of our approach in accurately identifying activities. By prioritizing non-deep-learning techniques and harnessing the strengths of passive RFID technology, our method offers a pragmatic and scalable solution for activity recognition in smart homes, significantly reducing dataset dependencies and human intervention requirements. Full article

Indoor location services often use Bluetooth low energy (BLE) devices for their low energy consumption and easy implementation. Applications like device monitoring, ranging, and asset tracking utilize the received signal strength (RSS) of the BLE signal to estimate the proximity of a device from the receiver. However, in multipath environments, RSS-based solutions may not provide an accurate estimation. In such environments, receivers with antenna arrays are used to calculate the difference in time of flight (ToF) and therefore calculate the direction of arrival (DoA) of the Bluetooth signal. Other techniques like triangulation have also been used, such as having multiple transmitters or receivers as a network of sensors. To find a lost item, devices like Tile^© use an onboard beeper to notify users of their presence. In this paper, we present a system that uses a single-measurement device and describe the method of measurement to estimate the location of a BLE device using RSS. A BLE device is configured as an Eddystone beacon for periodic transmission of advertising packets with RSS information. We developed a smartphone application to read RSS information from the beacon, designed an algorithm to estimate the DoA, and used the phone’s internal sensors to evaluate the DoA with respect to true north. The proposed measurement method allows for asset tracking by iterative measurements that provide the direction of the beacon and take the user closer at every step. The receiver application is easily deployable on a smartphone, and the algorithm provides direction of the beacon within a 30° range, as suggested by the provided results. Full article

In this paper, new path optimization algorithms are developed for uncrewed aerial vehicle (UAV) self-localization and target tracking, exploiting beacon (landmark) bearings and angle-of-arrival (AOA) measurements from a manoeuvring target. To account for time-varying rotations in the local UAV coordinates with respect to the global Cartesian coordinate system, the unknown orientation angle of the UAV is also estimated jointly with its location from the beacon bearings. This is critically important, as orientation errors can significantly degrade the self-localization performance. The joint self-localization and target tracking problem is formulated as a Kalman filtering problem with an augmented state vector that includes all the unknown parameters and a measurement vector of beacon bearings and target AOA measurements. This formulation encompasses applications where Global Navigation Satellite System (GNSS)-based self-localization is not available or reliable, and only beacons or landmarks can be utilized for UAV self-localization. An optimal UAV path is determined from the optimization of the Bayesian Fisher information matrix by means of A- and D-optimality criteria. The performance of this approach at different measurement noise levels is investigated. A modified closed-form projection algorithm based on a previous work is also proposed to achieve optimal UAV paths. The performance of the developed UAV path optimization algorithms is demonstrated with extensive simulation examples. Full article