Search Results (104)

Anti-collision detection technologies primarily rely on optical, radar, or laser sensors; however, their performance often deteriorates severely under adverse weather conditions (e.g., rain, snow, fog) or in scenarios involving visual occlusion. By contrast, magnetic anomaly detection leverages perturbations in the geomagnetic field induced by target objects (e.g., vehicles, metallic obstacles), exhibiting intrinsic all-weather operability and strong anti-interference capability. Nevertheless, conventional magnetic anomaly detection methods suffer from the limited applicability of the magnetic dipole model, which only affords coarse positioning accuracy and is predominantly suited for long-range targets. To address this limitation, this paper proposes an Extended N-th-Pole Magnetic Dipole (E-NMD) model that improves accuracy by analyzing the Lagrangian cosine term and rigorously constraining truncation errors under specific operational conditions. Experimental results demonstrate that, for steel with a relative permeability of 200, the model achieves a fitting variance of 99.87%. Furthermore, to overcome the inversion difficulties arising when the strength of short-range magnetic anomalies is comparable to sensor noise, an Adaptive Iterative Extended Kalman Filter (AI-EKF) is developed to enable robust noise suppression and precise distance estimation. Results indicate that E-NMD outperforms the traditional N-th-Pole Magnetic Dipole (NMD) model in proximal state estimation, achieving a 39.62% reduction in Root Mean Square Error (RMSE). Finally, in light of parameter uncertainty in magnetic anomaly targets under real-world conditions, a Dual-Mode Pairwise Iterative Extended Kalman Filter (DI-EKF) is introduced to jointly estimate parameters and system states, yielding an 89% reduction in RMSE compared to AI-EKF. Full article

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

There is increasing interest in space domain awareness (SDA), motivating the use of non-traditional sensors for space surveillance. One such sensor is the Buckland Park Stratospheric–Tropospheric (BPST) very high frequency (VHF) radar, which has demonstrated an ability to detect over 2000 resident space objects (RSO) daily. A by-product of the RSO observations is the measurement of ionospheric group retardation, which can be used to estimate the total electron content (TEC) between the ground and the satellite altitude. This paper describes the use of BPST radar observations of Starlink satellites to measure vertical TEC (vTEC) from the ground to 490 km and from the ground to 560 km. The variation in BPST radar vTEC is demonstrated for both geomagnetically quiet and storm periods. The results are combined with global ionospheric TEC maps to calculate the ratio of the ionospheric to plasmaspheric (or LEO to GPS) vTEC. This allows investigation of the diurnal and annual variation in the LEO to GPS vTEC for the radar location at a temporal resolution unavailable to LEO satellite-based measurements. The results indicate that the RMS uncertainty of the BPST radar vTEC estimates is 0.41 TEC units (TECU), comparing favorably with the ≈2 TECU RMS uncertainty typically measured by GNSS receivers. The technique described in this paper may be applied to any ST or boundary layer (BL) radar without the need for hardware changes. Full article

Geomagnetic navigation is a promising alternative for positioning and localization of UAV swarm systems in GNSS-denied environments. However, strong and heterogeneous electromagnetic interference generated by onboard power, propulsion, and electronic subsystems severely degrades magnetic measurement fidelity, limiting the achievable accuracy of cooperative UAV swarm navigation. To address this challenge, this paper proposes PG-TLNet, a physics-guided aeromagnetic interference compensation framework based on the extended Tolles–Lawson (T–L) model. By integrating onboard state information (current, voltage, and attitude) with magnetic measurements through physics-consistency constraints and a lightweight multi-branch convolutional neural network, the framework enables robust real-time compensation under strong and time-varying interference while remaining suitable for resource-constrained UAV nodes. Experimental validation using multiple scalar magnetometers under heterogeneous interference conditions, with amplitudes up to 1000 nT, shows that PG-TLNet consistently outperforms the conventional T–L model across all sensing nodes, maintaining residual magnetic interference at approximately 0–30 nT under long-duration and highly dynamic operations. The proposed method achieves an improvement ratio (IR) of up to 15 with an end-to-end inference latency below 94 μs. These results indicate that PG-TLNet meets the practical measurement fidelity requirements for geomagnetic navigation in GNSS-denied environments. By ensuring reliable and consistent magnetic measurements at the individual UAV node level, the proposed framework establishes a practical sensing foundation for geomagnetic navigation and distributed magnetic sensing in UAV swarm systems operating in GNSS-denied environments. Full article

Space weather monitoring depends critically on passive sensor systems that detect and measure natural solar and geospace emissions without transmitting radio frequency energy. These include riometers, solar radio monitors, interplanetary scintillation detectors, GNSS-based ionospheric sensors, and broadband solar spectrographs that enable the provision of critical data required to forecast geomagnetic storms, protect critical infrastructures, and support aviation services, satellite operations, and defense services. However, with the increasing proliferation of radiocommunication technologies such as 5G/6G networks, dense HF/VHF/UHF deployments, and large constellations of low-Earth-orbit (LEO) satellites, the interference threat to these exceptionally sensitive receivers has grown. Most of these operate near the thermal noise floor and thus require strict protection criteria to ensure continuity of data. This review and perspective article provides a cross-disciplinary synthesis of scientific requirements, documented RFI case studies, and ongoing regulatory developments related to spectrum protection for passive space weather sensors. It systematically integrates perspectives on physical, technical, and regulatory aspects that are typically addressed separately in the literature. The article reviews the operating principles of major sensor classes and analyzes documented RFI cases affecting GNSS, riometers, CALLISTO, BINGO, and systems impacted by LEO satellite emissions, drawing from existing reports and regulatory submissions. Building on this evidence base, the work comparatively evaluates regulatory methods under consideration for WRC-27 shows that hybrid approaches combining primary allocations in core observation bands with secondary status and coordination procedures in adjacent bands offer the most viable path forward. This synthesis contextualizes and analyzes how technical protection criteria can be integrated with existing and evolving regulatory instruments to inform spectrum governance. The study concludes that without coordinated international spectrum management incorporating explicit protection thresholds and registration procedures, the long-term viability of space weather monitoring infrastructure faces significant risk in an increasingly congested radio frequency environment. Full article

To overcome the limitations of Orthonormal Basis Function (OBF) methods in magnetic anomaly detection, including high false alarm rates and ambiguous target localization due to background noise, this paper introduces a high-confidence detection algorithm based on hierarchical clustering with an optimal cut height. The core of our approach is a theoretically derived optimal cut height, which is calculated from a physical model of the magnetic dipole’s vertical gradient field. This model establishes the implicit functional relationship between the effective detection range and key parameters, including magnetic moment orientation, geomagnetic inclination, and sensor height. The calculated optimal cut height serves as the critical criterion in a complete-linkage hierarchical clustering algorithm, which processes the alarm point clouds generated by a preliminary Greatest-of Cell-Averaging Constant False Alarm Rate (GOCA-CFAR) detector. This effectively suppresses isolated false alarms caused by background fluctuations while preserving spatially coherent alarm clusters within the target’s effective detection range, thereby significantly enhancing detection confidence. Results from both simulations and field experiments validate the efficacy of the proposed algorithm, demonstrating its superior capability to reliably discriminate genuine targets from false alarms compared to traditional one-dimensional CFAR detection. Full article

The upper atmospheric microwave sounding channels data are important for atmospheric data assimilation and retrieval. However, radiative transfer simulation accuracy is constrained by the precise characterization of the Zeeman splitting effect. This study investigates key influencing factors in upper-atmospheric microwave radiance simulations, focusing on the geomagnetic field parameters and the Zeeman splitting absorption coefficients. A three-dimensional (3D) atmosphere-magnetic coupling dataset is constructed using the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) version 2.0 Level 2A atmospheric profiles and the International Geomagnetic Reference Field (IGRF-13) as input for the microwave Line-by-Line (LBL) model. Observations from Special Sensor Microwave Imager/Sounder (SSMIS) channels 19 and 20 are used to quantitatively compare the effects of 2D and 3D geomagnetic fields on simulations and evaluate the impact of updated Zeeman splitting coefficients. Quantitative analysis reveals that the average vertical attenuation rate of geomagnetic field strength between 50 and 0.001 hPa is 2.98%, and using 3D magnetic field parameters improves the observation and simulation bias (O-B) for SSMIS channels 19 and 20 by approximately 3.67% and 3.52%, respectively. The updated microwave LBL model, incorporating molecular self-spin interactions and higher-order Zeeman effects, reduces the mean absolute error (MAE) and root mean square error (RMSE) of the SSMIS channel 20 by approximately 2.7% and 2.25%, respectively. Experimental results indicate that the 7⁺ line within a 2 MHz frequency shift is sensitive to moderate magnetic field strength (0.35–0.55 Gauss), while the 1⁻ line is sensitive to strong magnetic fields (0.5–0.7 Gauss). This study demonstrates that optimizing geomagnetic field representation and Zeeman splitting coefficients can improve upper atmospheric microwave radiance simulation accuracy by detailed comparison with observations. Full article

Buried ferromagnetic targets such as unexploded ordnance generate an additional magnetic field to the main geomagnetic field, which manifests as a magnetic anomaly signal for localization. This paper presents an alternative scheme for localization by using a rotating magnetic sensor array and a joint optimization algorithm. Multiple magnetic sensors are integrated into an automated rotating measurement platform to achieve efficient and convenient data acquisition. To solve the target’s position coordinates, we combine quantum particle swarm optimization (QPSO) with the genetic algorithm (GA) to develop a joint optimization algorithm, which we name QPSO-GA. The proposed algorithm incorporates QPSO’s advantages of rapid convergence and local refined search with the advantages of global exploration and diversity preservation from the GA. Field experiments demonstrate that the proposed measurement system and algorithm achieve an average localization error of less than ten centimeters in a scenario with multiple sensors for multiple targets within a survey area of 4 m by 4 m, meeting general application requirements. Full article

Accurate Unmanned Aerial Vehicle (UAV) positioning is vital for swarm cooperation. However, this remains challenging in situations where Global Navigation Satellite System (GNSS) and other external infrastructures are unavailable. To address this challenge, we propose to use only the onboard Microelectromechanical System Inertial Measurement Unit (MIMU), Magnetic sensor, Monocular camera and Ultra-Wideband (UWB) device to construct a distributed and anchor-free cooperative localization system by tightly fusing the measurements. As the onboard UWB measurements under dynamic motion conditions are noisy and discontinuous, we propose an adaptive adjustment method based on chi-squared detection to effectively filter out inconsistent and false ranging information. Moreover, we introduce the pose-only theory to model the visual measurement, which improves the efficiency and accuracy for visual-inertial processing. A sliding window Extended Kalman Filter (EKF) is constructed to tightly fuse all the measurements, which is capable of working under UWB or visual deprived conditions. Additionally, a novel Multidimensional Scaling-MAP (MDS-MAP) initialization method fuses ranging, MIMU, and geomagnetic data to solve the non-convex optimization problem in ranging-aided Simultaneous Localization and Mapping (SLAM), ensuring fast and accurate swarm absolute pose initialization. To overcome the state consistency challenge inherent in the distributed cooperative structure, we model not only the UWB noisy uncertainty but also the neighbor agent’s position uncertainty in the measurement model. Furthermore, we incorporate the Covariance Intersection (CI) method into our UWB measurement fusion process to address the challenge of unknown correlations between state estimates from different UAVs, ensuring consistent and robust state estimation. To validate the effectiveness of the proposed methods, we have established both simulation and hardware test platforms. The proposed method is compared with state-of-the-art (SOTA) UAV localization approaches designed for GNSS-challenged environments. Extensive experiments demonstrate that our algorithm achieves superior positioning accuracy, higher computing efficiency and better robustness. Moreover, even when vision loss causes other methods to fail, our proposed method continues to operate effectively. Full article

Modern electronic devices, such as smartphones and drones, integrate various sensors to enable diverse sensor-based applications. Yet, sensor measurements exhibit significant variations across different device models, even under the same environment. These variations arise from sensor biases, which occur in three different types: offset bias (additive constant errors), scale bias (multiplicative proportional errors), and drift bias (time-dependent or temperature-dependent errors). Among the biases, in this paper we specifically target offset bias, which has the greatest impact in typical smartphone usage scenarios. This generally leads to performance degradation in sensor-based applications across various device models and instances. To understand the characteristics of the offset bias, we categorize sensors into sensors with and without absolute reference values. Sensors with absolute references enable direct calibration using theoretical true values, while sensors with relative references require different approaches depending on how sensor applications process the data. For scalar-based applications that determine the current state by comparing a sensor measurement against a pre-defined reference, the offset biases can be removed by the existing procedures using reference devices. However, for sequence-based applications that determine the current state by analyzing relative changes in a sequence, the offset bias issue has not been addressed yet. We propose initial value removal and mean removal algorithms that statically and dynamically remove the offset biases from the sensor data sequences for these sequence-based applications. We evaluate our bias normalization algorithms for two different use cases in a geomagnetic-based indoor positioning system (IPS). First, we evaluate the impact of our bias normalization algorithms on the positioning performance of our LSTM-based IPS. Without bias normalization, although the reference device (Galaxy S23 Plus) showed an average positioning error of 0.6 m, the other three smartphone models (Galaxy S22 Plus, iPhone 15, and iPhone 16 Pro) exhibited much worse positioning performance, with errors of 2.48 m, 18.21 m, and 13.13 m. However, after applying our bias normalization, the average positioning errors of all models dropped below 0.68 m. Second, we also evaluate the impact of the bias normalization on detecting whether the position of a smartphone is in a pocket or in a hand-held state. For this, we analyze the sequence of light sensor measurements. We improved the detection accuracy from 42.3% to 97.6% with bias normalization across all device models without requiring individual threshold settings. Full article

This work presents a validation process of a Helmholtz cage developed by the authors at AGH University of Krakow. This type of test stand can generate a near-uniform, precisely controlled magnetic field inside its workspace. This is a crucial tool for several applications, including calibration of magnetic sensors, testing magnetorquers, and hardware-in-the-loop tests of attitude determination and control systems of small satellites. Although many institutions develop Helmholtz cages, we found the literature on methods of validating the final accuracy and uniformity of the generated magnetic field somewhat lacking. In this research, we showcase an approach to perform 3D scans of the magnetic field inside the cage using a probe actuated by a robotic arm. With that method, we verified that the magnitude and angle nonuniformity of the magnetic field vectors in our cage are below 2 percent and 0.4°, respectively, for a wide range of control inputs. We also perform background magnetic field measurements to identify and quantify sources of magnetic disturbances coming from the outside of our system and propose methods of minimizing their impact. It turns out that careful design and building process of the cage and its power driver might not be sufficient to achieve the optimal performance. In our case, we found that some factors, if unmitigated, can cause an error of a few milligauss. Hopefully, this work will help other teams developing similar devices avoid at least some of the possible pitfalls. Full article

Divers’ navigation heavily depends on their experience and physical condition, and accidents caused by failure to return occur every year. To address this issue, we developed a navigation system for divers. This navigation system leverages Raspberry Pi and low-cost sensors, including an accelerometer, gyro sensor, geomagnetic sensor, and pressure gauge, to guide divers along predefined routes back to their starting point. The system employs a 20 Hz sampling frequency and applies high-pass filtering (HPF) to acceleration signals to eliminate gravitational interference. Velocity integration errors are corrected using the rate of pressure change, while impulse noise in accelerometer and geomagnetic sensors is removed via the Robust Kalman Filter (RKF). A time-varying system noise covariance matrix enhances accuracy during rotational states. Quaternion-based attitude avoids gimbal lock, with the Kalman Filter (KF) fusion of accelerometer/geomagnetic data mitigating gyro sensor drift. Forced oscillator trials achieved pitch/roll RMS errors of ±1.23° and ±0.26°. In Kanagawa, Japan, divers successfully navigated 44 waypoints (<5 m spacing) along a route with obstacles (30 m rope, Authors, reefs), with a start/end GNSS positioning error of 6.67 m. 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

In complex environments where satellite signals may be interfered with, it is difficult to achieve precise positioning of high-speed aerial vehicles solely through the inertial navigation system. To overcome this challenge, this paper proposes an NGO-ELM geomagnetic matching-assisted navigation algorithm, in which the Northern Goshawk Optimization (NGO) algorithm is used to optimize the initial weights and biases of the Extreme Learning Machine (ELM). To enhance the matching performance of the NGO-ELM algorithm, three improvements are proposed to the NGO algorithm. The effectiveness of these improvements is validated using the CEC2005 benchmark function suite. Additionally, the IGRF-13 model is utilized to generate a geomagnetic matching dataset, followed by comparative testing of five geomagnetic matching models: INGO-ELM, NGO-ELM, ELM, INGO-XGBoost, and INGO-BP. The simulation results show that after the airborne equipment acquires the geomagnetic data, it only takes 0.27 µs to obtain the latitude, longitude, and altitude of the aerial vehicle through the INGO-ELM model. After unit conversion, the average absolute errors are approximately 6.38 m, 6.43 m, and 0.0137 m, respectively, which significantly outperform the results of four other models. Furthermore, when noise is introduced into the test set inputs, the positioning error of the INGO-ELM model remains within the same order of magnitude as those before the noise was added, indicating that the model exhibits excellent robustness. It has been verified that the geomagnetic matching-assisted navigation algorithm proposed in this paper can achieve real-time, accurate, and stable positioning, even in the presence of observational errors from the magnetic sensor. Full article

With the widespread application of DC technology in data centers, renewable energy, electric transportation, and high-voltage direct current (HVDC) transmission, DC rectangular busbars are becoming increasingly important in power transmission systems due to their high current density and compact structure. However, space constraints make the deployment of conventional sensors challenging, highlighting the urgent need for miniaturized, non-contact current measurement technologies to meet the integration requirements of smart distribution systems. This paper proposes a field-source inversion-based contactless DC measurement method for rectangular busbars. The mathematical model of the magnetic field near the surface of the DC rectangular busbar is first established, incorporating the busbar eccentricity, rotation, and geomagnetic interference into the model framework. Subsequently, a magnetic field–current inversion model is constructed, and the DC measurement of the rectangular busbar is achieved by performing an inverse calculation. The effectiveness of the proposed method is validated by both simulation studies and physical experiments. Full article