Search Results (272)

Indoor positioning remains a recurrent and significant challenge in research. Unlike outdoor environments, where the Global Positioning System (GPS) provides reliable location information, indoor scenarios lack direct line-of-sight to satellites or cellular towers, rendering GPS inoperative and requiring alternative positioning techniques. Despite numerous approaches, indoor contexts with resource limitations, energy constraints, or physical restrictions continue to suffer from unreliable localization. Many existing methods employ a fixed number of reference anchors, which sets a hard balance between localization accuracy and energy consumption, forcing designers to choose between precise location data and battery life. As a response to this challenge, this paper proposes an energy-aware indoor positioning strategy based on Mobile Ad Hoc Networks (MANETs). The core principle is a self-adaptive control loop that continuously monitors the network’s positioning accuracy. Based on this real-time feedback, the system dynamically adjusts the number of active anchors, increasing them only when accuracy degrades and reducing them to save energy once stability is achieved. The method dynamically estimates relative coordinates by analyzing node encounters and contact durations, from which relative distances are inferred. Generalized Multidimensional Scaling (GMDS) is applied to construct a relative spatial map of the network, which is then transformed into absolute coordinates using reference nodes, known as anchors. The proposal is evaluated in a realistic simulated indoor MANET, assessing positioning accuracy, adaptation dynamics, anchor sensitivity, and energy usage. Results show that the adaptive mechanism achieves higher accuracy than fixed-anchor configurations in most cases, while significantly reducing the average number of required anchors and their associated energy footprint. This makes it suitable for infrastructure-poor, resource-constrained indoor environments where both accuracy and energy efficiency are critical. Full article

Since GPS (Global Positioning System) cannot meet accuracy requirements indoors, indoor Location-Based Services (LBSs) have become increasingly important. BLE (Bluetooth Low Energy) offers cost and accuracy advantages. Typically, the position fingerprinting method is used for indoor positioning. However, due to irregular reflection and absorption, the indoor environment introduces various offsets in Bluetooth RSSI (Received Signal Strength Indicator). This study analyzed the RSSI space and proposed a pre-processing workflow to improve position estimation accuracy by correcting offsets in RSSI space for BLE fingerprinting methods using machine learning. Experiments performed using different position estimation methods showed that the corrected data achieved a 6% improvement over the filter-only result. This study also evaluated the effects of different pre-processing and post-processing filters on positioning accuracy. Experiments were also conducted using a published dataset and showed similar results. Full article

Automated Valet Parking (AVP) systems require high-precision positioning, especially in indoor environments where Global Positioning System (GPS) is unavailable. Existing methods, which use markers installed on parking lot walls or ceilings, often encounter difficulties due to marker detection failures caused by complex parking behaviors, such as infrastructure constraints or perpendicular parking. This study proposes an optimized indoor positioning system for AVP using fiducial markers recognized by front and rear vehicle cameras. To enhance accuracy and robustness, an Extended Kalman Filter (EKF) fuses vehicle kinematic data with marker pose information. Critically, to address the issue of marker occlusion by the front camera during reverse parking, a novel camera switching algorithm employing a hysteresis pattern based on vehicle position, heading, and motion direction is introduced. This ensures continuous marker visibility and stable positioning during parking maneuvers. The system’s effectiveness was validated through simulations and extensive real-vehicle experiments in a real parking space. Results demonstrate that the EKF significantly reduces positioning errors compared to kinematic prediction alone, particularly during curved driving. Furthermore, the proposed camera switching algorithm successfully overcomes the limitations of a front-only camera system, significantly improving positioning accuracy (e.g., reducing RMS error by up to 25.0% in X and 17.6% in Y during parking) and eliminating instability observed with simpler switching logic. This research contributes a cost-effective and reliable positioning solution, advancing the feasibility of AVP systems in challenging indoor environments. Full article

With the widespread deployment of 5G technology, high-precision positioning in global navigation satellite system (GNSS)-denied environments is a critical yet challenging task for emerging 5G applications, enabling enhanced spatial resolution, real-time data acquisition, and more accurate geolocation services. Traditional methods relying on single-source measurements like received signal strength information (RSSI) or time of arrival (TOA) often fail in complex multipath conditions. To address this, the positional encoding multi-scale residual network (PE-MSRN) is proposed, a novel deep learning framework that enhances positioning accuracy by deeply mining spatial information from 5G channel state information (CSI). By designing spatial sampling with multigranular data and utilizing multi-source information in 5G CSI, a dataset covering a variety of positioning scenarios is proposed. The core of PE-MSRN is a multi-scale residual network (MSRN) augmented by a positional encoding (PE) mechanism. The positional encoding transforms raw angle of arrival (AOA) data into rich spatial features, which are then mapped into a 2D image, allowing the MSRN to effectively capture both fine-grained local patterns and large-scale spatial dependencies. Subsequently, the PE-MSRN algorithm that integrates ResNet residual networks and multi-scale feature extraction mechanisms is designed and compared with the baseline convolutional neural network (CNN) and other comparison methods. Extensive evaluations across various simulated scenarios, including indoor autonomous driving and smart factory tool tracking, demonstrate the superiority of our approach. Notably, PE-MSRN achieves a positioning accuracy of up to 20 cm, significantly outperforming baseline CNNs and other neural network algorithms in both accuracy and convergence speed, particularly under real measurement conditions with higher SNR and fine-grained grid division. Our work provides a robust and effective solution for developing high-fidelity 5G positioning systems. Full article

Global navigation satellite systems (GNSS) can provide high-quality location information in outdoor environments. In indoor environments, GNSS cannot achieve accurate and stable location information due to the obstruction and attenuation of buildings together with the influence of multipath effects. Due to the rapid development of micro-electro-mechanical system (MEMS) sensors, today’s smartphones are equipped with various low-cost and small-volume MEMS sensors. Therefore, it is of great significance to study indoor pedestrian positioning technology based on smartphones. In order to provide pedestrians with high-precision and reliable location information in indoor environments, we propose a pedestrian dead reckoning (PDR) method based on Transformer+TCN (temporal convolutional network). Firstly, we use IMU (inertial measurement unit)/PDR pre-integration to suppress the inertial navigation divergence. Secondly, we propose a step length estimation algorithm based on Transformer+TCN. The Transformer and TCN networks are superimposed to improve the ability to capture complex dependencies and improve the generalization and reliability of step length estimation. Finally, we propose factor graph optimization (FGO) models based on sliding windows (SW-FGO) to provide accurate posture, which use accelerometer (ACC)/gyroscope/magnetometer (MAG) data to establish factors. We designed a fusion positioning estimation test and a comparison test on step length estimation algorithm. The results show that the fusion method based on SW-FGO proposed by us improves the positioning accuracy by 29.68% compared with the traditional FGO algorithm, and the absolute position error of the step length estimation algorithm based on Transformer+TCN in pocket mode is mitigated by 42.15% compared with the LSTM algorithm. The step length estimation model error of Transformer+TCN is 1.61%, and the step length estimation accuracy is improved by 24.41%. Full article

Localization of Unmanned Aerial Vehicles (UAVs) in spaces with a limited availability of Global Navigation Satellite System signals presents a challenge, and one possible solution is the usage of Ultra-Wideband (UWB) transceivers as an aid in the localization process. This paper examines the influence of placing the UWB anchors on the UAVs’ localization accuracy in indoor spaces. Different testing scenarios, with variations in the number of anchors and their relative position towards the UAV, were created. Results show that the anchor placement plays an important role and is a significant factor in achieving accurate positioning of UAVs. The error for different testing configurations was shown through the RMSE for each axis, backed up by the standard deviation. The increase in the number of UWB anchors with the combined use of an additional laser ranging sensor for altitude measurement provided the best result. The RMSE was less than 18 cm in each axis of a 3D coordinate system with the standard deviation of up to 4.41 cm. For the testing scenarios that included the usage of a laser altimeter, the RMSE for the z-axis dropped below 1 cm, with the standard deviation of under 0.3 cm. Full article

Modern electronic devices such as smartphones, wearable devices, and robots typically integrate three-dimensional sensors to track the device’s movement in the 3D space. However, sensor measurements in three-dimensional vectors are highly sensitive to device orientation since a slight change in the device’s tilt or heading can change the vector values. To avoid complications, applications using these sensors often use only the magnitude of the vector, as in geomagnetic-based indoor positioning, or assume fixed device holding postures such as holding a smartphone in portrait mode only. However, using only the magnitude of the vector loses the directional information, while ad hoc posture assumptions work under controlled laboratory conditions but often fail in real-world scenarios. To resolve these problems, we propose a universal vector calibration algorithm that enables consistent three-dimensional vector measurements for the same physical activity, regardless of device orientation. The algorithm works in two stages. First, it transforms vector values in local coordinates to those in global coordinates by calibrating device tilting using pitch and roll angles computed from the initial vector values. Second, it additionally transforms vector values from the global coordinate to a reference coordinate when the target coordinate is different from the global coordinate by correcting yaw rotation to align with application-specific reference coordinate systems. We evaluated our algorithm on geomagnetic field-based indoor positioning and bidirectional step detection. For indoor positioning, our vector calibration achieved an 83.6% reduction in mismatches between sampled magnetic vectors and magnetic field map vectors and reduced the LSTM-based positioning error from 31.14 m to 0.66 m. For bidirectional step detection, the proposed algorithm with vector calibration improved step detection accuracy from 67.63% to 99.25% and forward/backward classification from 65.54% to 100% across various device orientations. Full article

Background: Motion capture (MoCap) technologies have transformed athlete monitoring, yet athletic departments face complex decisions when selecting systems for multiple sports. Methods: We conducted a narrative review of peer-reviewed studies (2015–2025) examining optical marker-based, inertial measurement unit (IMU) systems, including Global Navigation Satellite System (GNSS)-integrated systems, and markerless computer vision systems. Studies were evaluated for validated accuracy metrics across indoor court, aquatic, and outdoor field environments. Results: Optical systems maintain sub-millimeter accuracy in controlled environments but face field limitations. IMU systems demonstrate an angular accuracy of 2–8° depending on movement complexity. Markerless systems show variable accuracy (sagittal: 3–15°, transverse: 3–57°). Environmental factors substantially impact system performance, with aquatic settings introducing an additional orientation error of 2° versus terrestrial applications. Outdoor environments challenge GNSS-based tracking (±0.3–3 m positional accuracy). Critical gaps include limited gender-specific validation and insufficient long-term reliability data. Conclusions: This review proposes a tiered implementation framework combining foundation-level team monitoring with specialized assessment tools. This evidence-based approach guides the selection of technology aligned with organizational priorities, sport-specific requirements, and resource constraints. Full article

Indoor localization techniques operate independently of Global Navigation Satellite Systems (GNSSs), which are primarily designed for outdoor environments. However, integrating indoor and outdoor positioning often leads to inconsistent and delayed location estimates, especially at transition zones such as building entrances. This paper develops a probabilistic transition detection algorithm to identify indoor, outdoor, and transition zones, aiming to enhance the continuity and accuracy of positioning. The algorithm leverages multi-source sensor data, including WiFi Received Signal Strength Indicator (RSSI), Bluetooth Low-Energy (BLE) RSSI, and GNSS metrics such as carrier-to-noise ratio. During transitions, the system incorporates Inertial Measurement Unit (IMU)-based tracking to ensure smooth switching between positioning engines. The outdoor engine utilizes a Kalman Filter (KF) to fuse IMU and GNSS data, while the indoor engine employs fingerprinting techniques using WiFi and BLE. This paper presents experimental results using three distinct devices across three separate buildings, demonstrating superior performance compared to both Google’s Fused Location Provider (FLP) algorithm and a GPS. Full article

Pork accounts for 34% of global meat consumption, following poultry and beef. Intensive pig farming has expanded to meet increasing demand, but space constraints and poor environmental conditions can negatively affect pig welfare. This study aimed to investigate pigs’ spatial preferences in response to environmental factors in an experimental pig barn. Six 60-day-old Yorkshire pigs were observed for 60 days. Indoor temperature (IT), relative humidity (IRH), and CO₂ concentration (ICO₂) were measured hourly, and pig positions were recorded using an RGB 2D-IP camera. Pearson correlation analysis was performed using SPSS. IT ranged from 14.3 °C to 25.1 °C, IRH from 78.9% to 96.5%, and ICO₂ from 1038 to 1850 ppm. A strong negative correlation was found between IT and IRH (r = −0.89), while IT and ICO₂ were uncorrelated (r = −0.01). Pigs showed a clear preference for sections with lower IT, supporting previous findings on thermal preference. Structural features, such as two-wall enclosures, also influenced stay frequency. These results suggest that optimizing barn structure and improving ventilation and manure management can support thermal comfort and improve welfare in intensive pig farming systems. Full article

To address the challenges of low pesticide utilization, insufficient automation, and health risks in orchard plant protection, we developed an autonomous spraying vehicle using ArduPilot firmware and a robot operating system (ROS). The system tackles orchard navigation hurdles, including global navigation satellite system (GNSS) signal obstruction, light detection and ranging (LIDAR) simultaneous localization and mapping (SLAM) error accumulation, and lighting-limited visual positioning. A key innovation is the integration of an extended Kalman filter (EKF) to dynamically fuse T265 visual odometry, inertial measurement unit (IMU), and GPS data, overcoming single-sensor limitations and enhancing positioning robustness in complex environments. Additionally, the study optimizes PID controller derivative parameters for tracked chassis, improving acceleration/deceleration control smoothness. The system, composed of Pixhawk 4, Raspberry Pi 4B, Silan S2L LIDAR, T265 visual odometry, and a Quectel EC200A 4G module, enables autonomous path planning, real-time obstacle avoidance, and multi-mission navigation. Indoor/outdoor tests and field experiments in Sun Village Orchard validated its autonomous cruising and obstacle avoidance capabilities under real-world orchard conditions, demonstrating feasibility for intelligent plant protection. Full article

Progressive global aging has increased the number of elderly individuals living alone. The consequent rise in fall accidents has worsened physical injuries, reduced the quality of life, and increased medical expenses. Existing wearable fall-detection devices may cause discomfort, and camera-based systems raise privacy concerns. Here, we propose a non-contact fall-detection system that integrates 4D imaging radar sensors with artificial intelligence (AI) technology to detect falls through real-time monitoring and visualization using a web-based dashboard and Unity engine-based avatar, along with immediate alerts. The system eliminates the need for uncomfortable wearable devices and mitigates the privacy issues associated with cameras. The radar sensors generate Point Cloud data (the spatial coordinates, velocity, Doppler power, and time), which allow analysis of the body position and movement. A CNN model classifies postures into standing, sitting, and lying, while changes in the speed and position distinguish falling actions from lying-down actions. The Point Cloud data were normalized and organized using zero padding and k-means clustering to improve the learning efficiency. The model achieved 98.66% accuracy in posture classification and 95% in fall detection. This study demonstrates the effectiveness of the proposed fall detection approach and suggests future directions in multi-sensor integration for indoor applications. Full article

Wireless indoor positioning systems are challenged by the reliance on densely deployed hardware and exhaustive site surveys, leading to elevated deployment and maintenance costs that limit scalability. This paper introduces a novel positioning framework that enhances the existing In-Building Wireless (IBW) infrastructure by retransmitting Global Navigation Satellite System (GNSS) signals. Pseudorange residuals extracted from raw GNSS measurements, when mapped against known cable lengths, facilitate anchor identification and precise ranging. In parallel, directional and inertial measurements are derived from the channel state information (CSI) of cellular reference signals. Building upon these observations, we develop a Hybrid Adaptive Filter-Graph Fusion (HAF-GF) algorithm for high-precision positioning, wherein the adaptive filter modulates observation noise based on Line-of-Sight (LoS) conditions, while a factor graph optimization over multiple positional constraints ensures global consistency and accelerates convergence. Ray tracing-based simulations in a complex office environment validate the efficacy of the proposed approach, demonstrating a 30% improvement in positioning accuracy and at least a threefold increase in deployment efficiency compared to conventional methods. 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

Airports are complex environments where efficient localization and intelligent traffic management are essential for ensuring smooth navigation and operational efficiency for both pedestrians and Autonomous Guided Vehicles (AGVs). This study presents an Artificial Intelligence (AI)-driven airport traffic management system that integrates Visible Light Communication (VLC), rerouting techniques, and adaptive reward mechanisms to optimize traffic flow, reduce congestion, and enhance safety. VLC-enabled luminaires serve as transmission points for location-specific guidance, forming a hybrid mesh network based on tetrachromatic LEDs with On-Off Keying (OOK) modulation and SiC optical receivers. AI agents, driven by Deep Reinforcement Learning (DRL), continuously analyze traffic conditions, apply adaptive rewards to improve decision-making, and dynamically reroute agents to balance traffic loads and avoid bottlenecks. Traffic states are encoded and processed through Q-learning algorithms, enabling intelligent phase activation and responsive control strategies. Simulation results confirm that the proposed system enables more balanced green time allocation, with reductions of up to 43% in vehicle-prioritized phases (e.g., Phase 1 at C1) to accommodate pedestrian flows. These adjustments lead to improved route planning, reduced halting times, and enhanced coordination between AGVs and pedestrian traffic across multiple intersections. Additionally, traffic flow responsiveness is preserved, with critical clearance phases maintaining stability or showing slight increases despite pedestrian prioritization. Simulation results confirm improved route planning, reduced halting times, and enhanced coordination between AGVs and pedestrian flows. The system also enables accurate indoor localization without relying on a Global Positioning System (GPS), supporting seamless movement and operational optimization. By combining VLC, adaptive AI models, and rerouting strategies, the proposed approach contributes to safer, more efficient, and human-centered airport mobility. Full article