Geomagnetic Sensor Research Articles

Ballistic Fitting Impact Point Prediction Based on Improved Crayfish Optimization Algorithm

To solve the problem of difficulty in predicting the impact point clearly and promptly during projectile flight, this paper proposes an improved ballistic-impact-point prediction method. A certain type of high-spinning tailed projectile is taken as the research object for online real-time landing point prediction research. This study comprehensively utilizes the real-time radar measurement data and the geomagnetic data measured by the bomb-carried geomagnetic sensor. It applies the four-degree-of-freedom ballistic model to predict the landing point. First, the roll angular velocity is calculated based on the geomagnetic data, after which the radar real-time measurement data are segmentally fitted using the improved crayfish algorithm. Then, the fitted parameters are substituted into the four-degree-of-freedom ballistic model. Finally, the C-K method is used to identify the aerodynamic parameters, and the identified aerodynamic parameters are used for fallout prediction. The simulation results show a small deviation between the predicted and actual impact points using the improved ballistic-impact-point prediction method.

Performance Evaluation of a Bioinspired Geomagnetic Sensor and Its Application for Geomagnetic Navigation in Simulated Environment.

For geomagnetic navigation technology, taking inspiration from nature and leveraging the principle of animals' utilization of the geomagnetic field for long-distance navigation, and employing biomimetic technology to develop higher-precision geomagnetic sensors and more advanced navigation strategies, has emerged as a new trend. Based on the two widely acknowledged biological magnetic induction mechanisms, we have designed a bioinspired weak magnetic vector (BWMV) sensor and integrated it with neural networks to achieve geomagnetic matching navigation. In this paper, we assess the performance of the BWMV sensor through finite element model simulation. The result validates its high measurement accuracy and outstanding adaptability to installation errors with the assistance of specially trained neural networks. Furthermore, we have enhanced the bioinspired geomagnetic navigation algorithm and proposed a more advanced search strategy to adapt to navigation under the condition of no prior geomagnetic map. A simulated geomagnetic navigation platform was constructed based on the finite element model to simulate the navigation of the BWMV sensor in geomagnetic environments. The simulated navigation experiment verified that the proposed search strategy applied to the BWMV sensor can achieve high-precision navigation. This study proposes a novel approach for the research of bioinspired geomagnetic navigation technology, which holds great development prospects.

Position-Aware Indoor Human Activity Recognition Using Multisensors Embedded in Smartphones.

Composite indoor human activity recognition is very important in elderly health monitoring and is more difficult than identifying individual human movements. This article proposes a sensor-based human indoor activity recognition method that integrates indoor positioning. Convolutional neural networks are used to extract spatial information contained in geomagnetic sensors and ambient light sensors, while transform encoders are used to extract temporal motion features collected by gyroscopes and accelerometers. We established an indoor activity recognition model with a multimodal feature fusion structure. In order to explore the possibility of using only smartphones to complete the above tasks, we collected and established a multisensor indoor activity dataset. Extensive experiments verified the effectiveness of the proposed method. Compared with algorithms that do not consider the location information, our method has a 13.65% improvement in recognition accuracy.

A novel method for measuring roll angle

The precise measurement of the spin speed of a high-speed autonomous unmanned aerial vehicle (HSA-UAV) is a key element in mastering flight stability, and the measurement of roll angle is the key to determining the accuracy of navigation control systems. We put forward a novel method for measuring roll angle. This method starts from the time–frequency domain analysis of the output signal of the geomagnetic sensor, extracts the time–frequency ridge of the time–frequency matrix (TFM) to obtain the spin speed of the HSA-UAV, and reconstructs the output signal of the geomagnetic sensor. It can calculate the roll angle when the calibration parameters of the geomagnetic sensor are unknown and have good engineering practical value. In addition, we also propose an improved nonlinear short-time Fourier transform with high-frequency resolution and a forward penalty dynamic path ridge-extraction method with frequency jump suppression to extract instantaneous frequency in the TFM.

Improving the systems for controlling ground-based sun orientation devices

The control system for terrestrial two-axis devices for orientation to the Sun has been improved with a high-speed microcontroller operation algorithm. A geomagnetic sensor was introduced into the system to increase the reliability of monitoring the positioning of solar cells. The basis of the algorithm is a simplified astronomical and geographical model of the movement of the Sun in the celestial sphere. The control system automatically tracks the trajectory of the Sun and calculates its angular coordinates for the current moment of time on any day of the year and for any point on the globe. The derived equations of the simplified mathematical model are suitable for calculations of orientation angles to the Sun in real time on 8-bit microcontrollers with low computing power. The control system by AVR-328 microcontrollers was studied. It was established that the use of the algorithm when programming microcontrollers for two-axis orientation systems ensures high stability and reliability of the tracker`s functioning process. The technical parameters of the AVR-328 microcontrollers in the case of using the developed algorithm ensure that the control system performs one reorientation step in a time interval of less than 2 seconds, which ensures the minimum technical period of the reorientation process by the tracker drive mechanisms which is about 5 seconds. Deviations of the calculated orientation angle from the exact value do not exceed 3°, which corresponds to the relative accuracy of recording the solar radiation intensity, which is less than 0.3 %. The microcontroller program written according to the developed simplified algorithm occupies about 35 % of its memory. Therefore, the use of the developed algorithm frees up the resources of AVR-328 microcontrollers for performing additional data processing operations and automatic control over various additional devices related to the process of orientation to the Sun. In the case of solar energy, the algorithm ensures the use of about 98 % of the power of solar radiation

Improved ellipsoid fitting aided geomagnetic sensor calibration algorithm

Aiming at the problems that the traditional single ellipsoid fitting fails to eliminate the errors of the geomagnetic sensor and does not consider the geomagnetic anomaly information, etc., this paper proposes a robust least squares combined with the Levenberg–Marquardt (LM-RLS) algorithm to fit an accurate ellipsoid to achieve accurate data calibration. According to the geomagnetic sensor error model, the total amount of geomagnetism is used as the constraint, the fitted ellipsoid coefficients of RLS are used as the initial values, and the LM algorithm is used to calculate the magnetic field dispersion points to the nearest point of the fitted ellipsoid surface. Then, the initial calibration value is solved by the criterion of the minimum weighted sum of squares of the closest point distance. Finally, the accurate ellipsoid is obtained to realize data calibration. The results of the dynamic experiment show that after the ellipsoid is precisely fitted by the LM-RLS algorithm, the sum of the absolute values of the distance from the magnetic field discrete data to the ellipsoid surface is reduced by 48.33% (RLS) and 43.10% (RWTLS). The relative errors of the magnetic field in the X, Y, and Z axes are reduced from 0.48%, 0.78%, 1.48% (RLS) and 0.30%, 0.58%, 1.14% (RWTLS) to 0.10%, 0.04% 0.16 %, respectively. The accuracy of geomagnetic calibration is improved by 68.06% (RLS) and 61.02% (RWTLS) in the dynamic experiment. The algorithm improves the accuracy of geomagnetic measurement, which provides a new idea for the calibration of three-axis geomagnetic sensors.

Application of bioinspired geomagnetic sensor measurements and geomagnetic map modeling based on neural networks in simulated navigation

A geomagnetic field is a vector field in which the strength and direction are related to geographical location. Geomagnetic navigation technology, which uses collected geomagnetic field information to achieve positioning and navigation, has the advantages of reliability, stability, accuracy, and concealment. With the deepening research on geomagnetic navigation, bioinspired geomagnetic navigation technology has also been developed, which mainly studies and imitates the magnetic sensing mechanism and navigation behavior of animals, providing new research ideas for geomagnetic navigation technology. The magnetic particle hypothesis and free radical pair hypothesis are two mainstream mechanisms of biological sensing using the geomagnetic field, and studies have shown that these two mechanisms may be coupled within organisms. In this study, we propose a bioinspired weak magnetic vector (BWMV) sensor based on the joint sensing mechanism of magnetic particles and free radicals. It consists of a magnetic rod made of soft magnetic material and a tunnel magnetoresistance (TMR) sensor array. A magnetic rod was used to simulate magnetic particles to convert magnetic field angle information into magnetic field intensity distribution information, and the TMR sensor array was used to simulate the perception of the magnetic field distribution by free radicals. In addition, artificial neural networks (ANNs) were used for BWMV sensors to obtain the mapping relationship between the magnetic field distribution and parameters, which can be used for geomagnetic navigation. To verify the navigation effect of the BWMV sensor in the laboratory, a simulated geomagnetic navigation device was built, and the high-precision mapping relationship from geomagnetic parameters to latitude and longitude information of the selected navigation area was obtained through another ANN. Finally, the effectiveness of the BWMV sensor based on ANNs for geomagnetic navigation is verified using simulated navigation experiments.

Azimuth estimation based on CNN and LSTM for geomagnetic and inertial sensors data

Although estimating the azimuth using a geomagnetic sensor is very useful, the estimation error may be very large due to the surrounding geomagnetic disturbance. We proposed a novel method for preprocessing appropriately for geomagnetic and inertial sensor data to be suitable for the proposed Artificial Neural Network model and training method for the model. As a result, the probability of azimuth estimation error within 1 degree is 96.4% with regression estimation. For classification estimation, when the azimuth estimation probability is 90% or more, the probability that the azimuth estimation error is within 1 degree is 100%.

Indoor Localization Based on Integration of Wi-Fi with Geomagnetic and Light Sensors on an Android Device Using a DFF Network

Sensor-related indoor localization has attracted considerable attention in recent years. The accuracy of conventional fingerprint solutions based on a single sensor, such as a Wi-Fi sensor, is affected by multipath interferences from other electronic devices that are produced as a result of complex indoor environments. Light sensors and magnetic (i.e., geomagnetic) field sensors can be used to enhance the accuracy of a system since they are less vulnerable to disturbances. In this paper, we propose a deep feedforward (DFF)-neural-network-based method, termed DFF-WGL, which integrates the data from the embedded Wi-Fi sensor, geomagnetic field sensor, and light sensor (WGL) in a smart device to localize the device in an indoor environment. DFF-WGL does not require complex and expensive auxiliary equipment, except for basic fluorescent lamps and low-density Wi-Fi signal coverage, conditions that are easily satisfied in modern offices or educational buildings. The proposed system was implemented on a commercial off-the-shelf android device, and performance was evaluated through an experimental analysis conducted in two different indoor testbeds, one measuring 60.5 m2 and the other measuring 38 m2, with 242 and 60 reference points, respectively. The results indicate that the model prediction with an input consisting of the combination of light, a magnetic field sensor, and two Wi-Fi RSS signals achieved mean localization errors of 0.01 m and 0.04 m in the two testbeds, respectively, compared with any subset of combination of sensors, verifying the effectiveness of the proposed DFF-WGL method.

Design and Implementation of 4WD Intelligent Car System Based on Integrated Navigation

Focuses on the basic principle of integrated navigation system based on geomagnetic sensor, three-axis gyroscope and three-axis accelerometer. A four-wheel independent driving car platform is designed with STM32F4 MCU as the control core and RZ7899 as the motor drive. The vehicle heading angle is obtained through the quaternion obtained by MPU6050, and combined with the geomagnetic parameters, the serial double PID control algorithm is used for navigation by correcting the deflection angle, the intelligent car can run in a straight line without other navigation systems within a certain range.

TN-GAN-Based Pet Behavior Prediction through Multiple-Dimension Time-Series Augmentation.

Behavioral prediction modeling applies statistical techniques for classifying, recognizing, and predicting behavior using various data. However, performance deterioration and data bias problems occur in behavioral prediction. This study proposed that researchers conduct behavioral prediction using text-to-numeric generative adversarial network (TN-GAN)-based multidimensional time-series augmentation to minimize the data bias problem. The prediction model dataset in this study used nine-axis sensor data (accelerometer, gyroscope, and geomagnetic sensors). The ODROID N2+, a wearable pet device, collected and stored data on a web server. The interquartile range removed outliers, and data processing constructed a sequence as an input value for the predictive model. After using the z-score as a normalization method for sensor values, cubic spline interpolation was performed to identify the missing values. The experimental group assessed 10 dogs to identify nine behaviors. The behavioral prediction model used a hybrid convolutional neural network model to extract features and applied long short-term memory techniques to reflect time-series features. The actual and predicted values were evaluated using the performance evaluation index. The results of this study can assist in recognizing and predicting behavior and detecting abnormal behavior, capacities which can be applied to various pet monitoring systems.

Deep Learning Aided Magnetostatic Fields Based Real-Time Pose Estimation of AUV for Homing Applications

Autonomous underwater vehicles (AUVs) require a submerged docking station (DS) for recharging their batteries and data transfer purposes, in order to increase the vehicle endurance and updating its missions in the current subsea scenarios. Hence, a precise short-range guidance toward the DS is needed for AUVs for a reliable homing process in dynamic ocean environments. In this letter, a deep learning (DL) aided short-range guidance technique is proposed for reliable and precise homing of AUVs using an intelligent magnetic field guidance. A magnetic field is generated up to 20 m range from the dipole coil wound on the DS. The 64 bit microcontroller-based data acquisition system with a three-axis geomagnetic sensor embedded with DL hardware is developed as a prototype for estimating the AUV pose relative to the DS. A DL model is trained with the acquired magnetic field data with the coordinates of the AUV. The customized DL model is envisaged for predicting range and bearing angle simultaneously and has been developed with required hidden layers and activation functions. In this problem, the optimal DL model with two hidden layers and 64 neurons provides 94% accuracy in the prediction of AUV pose parameters i.e., range and bearing angle relative to DS. The DL model is embedded with the AUV hardware for real-time pose estimation of the AUV toward the DS. The proposed DL-based methodology is robust for AUV homing applications when compared to conventional approaches and machine learning techniques in dynamic environments.

Recent advances in studies on magnetosome-associated proteins composing the bacterial geomagnetic sensor organelle.

Magnetotactic bacteria (MTB) generate a membrane-enclosed subcellular compartment called magnetosome, which contains a biomineralized magnetite or greigite crystal, an inner membrane-derived lipid bilayer membrane, and a set of specifically targeted associated proteins. Magnetosomes are formed by a group of magnetosome-associated proteins encoded in a genomic region called magnetosome island. Magnetosomes are then arranged in a linear chain-like positioning, and the resulting magnetic dipole of the chain functions as a geomagnetic sensor for magneto-aerotaxis motility. Recent metagenomic analyses of environmental specimens shed light on the sizable phylogenetical diversity of uncultured MTB at the phylum level. These findings have led to a better understanding of the diversity and conservation of magnetosome-associated proteins. This review provided an overview of magnetosomes and magnetosome-associated proteins and introduced recent topics about this fascinating magnetic bacterial organelle. This article is protected by copyright. All rights reserved.

Application of IMMF–IHHT algorithm to suppressing random interference of geomagnetic sensors

Aiming at the problem that the geomagnetic sensor is vulnerable to external interference in the navigation process, this paper analyzes the frequency distribution range of geomagnetic signal and the noise characteristics in geomagnetic signal and proposes an improved morphological filtering and Hilbert–Huang transform (IMMF–IHHT) algorithm to extract and recognize the features of geomagnetic measurement signal. To avoid frequency aliasing and distortion caused by empirical mode decomposition, an improved morphological filtering algorithm based on mean constraint is used to preprocess the measured signal. The Hilbert spectrum of the decomposed signal is solved, the signal components are discriminated by the similarity criterion, and the signal components in line with the frequency range of the geomagnetic signal are extracted and processed to reconstruct the geomagnetic measurement signal. Simulation and experiments show that the signal-to-noise ratio and root-mean-square error of IMMF–IHHT combination algorithm are better than MF-HHT combination algorithm and IHHT algorithm. This algorithm has good signal feature extraction and recognition ability.

Global Perception-Based Robust Parking Space Detection Using a Low-Cost Camera

In the city, parking space detection can help car owners save time in finding parking spaces, and it can also provide help for autonomous parking. However, existing parking space detection methods based on embedded geomagnetic sensors are usually costly and complex to deploy in a large area. To reduce costs while obtaining sufficient performance, it is desirable to detecting parking spaces with cameras. We adopt a low-cost visual method to solve the above problems, using a convolutional neural network to achieve the parking space classification. We propose a general module called Global Perceptual Feature Extractor (GPFE) based on transformer to achieve global attention, which can be easily combined with other classification networks to improve the accuracy and robustness of the entire system, and alleviate the impact of illumination changing and other issues. We verify the proposed method with other methods on three public parking space detection datasets (CNREXT, PKLot and ACPDS). The results show that the accuracy of the network combined with the GPFE module has been generally improved, especially on scenes with low and high illumination, which verifies the robustness and performance of the module.

Suppressing the Saturated Negative Effects to Recover the Effective Sensing FID Signal From an Overhauser Magnetometer via Segmented Linear Regression

The free induction decay (FID) signal of Overhauser geomagnetic sensors may suffer from saturation distortion in the mobile magnetic survey, which results in the loss of signal envelope information. Aiming to suppress the negative effects of saturation, this article proposes a recovery method for the FID signal from saturation based on segmented linear regression. The influences of the signal-to-noise ratio (SNR) and the saturation level on the recovery effect of the proposed method were investigated. Numerical simulations show that the proposed method has a good recovery performance under low SNR and high saturation levels. Furthermore, we set up an Overhauser geomagnetic sensor experimental test platform and conducted extensive experiments. The experimental results show that when the FID signal was partially saturated with an SNR of 20.47 dB, the initial amplitude and the relaxation time recovery error were reduced by a factor of 7.30 and 6.76, respectively. Even if the FID signal is fully saturated, the proposed method can still obtain the initial amplitude and the relaxation time accurately.

CGA-VLP: High Accuracy Visible Light Positioning Algorithm Using Single Square LED with Geomagnetic Angle Correction

Visible light positioning (VLP), benefiting from its high accuracy and low cost, is a promising technology for indoor location-based services. In this article, the theoretical limits and error sources of traditional camera-based VLP systems are analyzed. To solve the problem that multiple LEDs are required and auxiliary sensors are imperfect, a VLP system with a single square LED which can correct the geomagnetic angle obtained from a geomagnetic sensor is proposed. In addition, we conducted a static positioning experiment and a dynamic positioning experiment integrated with pedestrian dead reckoning on an Android platform to evaluate the effectiveness of the proposed method. According to the experimental results, when the horizontal distance between the camera and the center of the LED is less than 120 cm, the average positioning error can be retained within 10 cm and the average positioning time on the mobile phone is 39.64 ms.

Attitude Magnetic Measurement Compensation Method of Fiber-Optic Submarine Seismometer

When the optical fiber submarine seismometer is applied to the seabed, the direction of vibration should be compensated by heading angle. In order to meet the stringent requirements of small size and low power consumption, geomagnetic sensors are used to measure the heading angle. Due to the complexity of magnetic field interference in the seismometer, the heading angle calculated by electronic compass based on the geomagnetic sensors is not accurate. In order to improve the accuracy of heading angle, we propose a geomagnetic compensation method based on unscented Kalman algorithm, which can solve the problems of difficult calibration operation and long-term accuracy in existing magnetic compensation methods. Meanwhile, according to the environmental conditions of underwater applications, we further optimize the method to reduce the calculation amount and avoid the non-positive definite problem of some Cholesky decomposition. The experiment is carried out to validate the effectiveness of the proposed method. The results show that the heading angle errors compensated by proposed method are all within 0.5°, which meets the application requirements of fiber optic submarine seismometer. In addition, the experimental operation is simple when using the proposed method.

Parking Guidance System Based on Geomagnetic Sensors and Recurrent Neural Networks

The increase of motor vehicles year by year has led to a number of parking difficulties and traffic congestion problems. The intelligent parking system can effectively alleviate the parking difficulties and has received wide attention. Geomagnetic sensors are widely used due to their low cost and easy deployment. However, traditional geomagnetic parking detection algorithms cannot cope with complex parking behaviors and have low vehicle detection performance. Therefore, in this paper, a new parking guidance system is proposed by integrating related technologies such as ZigBee, geomagnetic sensor, and RNN. With our limited knowledge, in the research branch of the parking guidance system, RNN is applied to geomagnetic vehicle detection for the first time to detect the status of parking spaces and obtain more accurate identification results of geomagnetic signals. The training data is obtained from real scenarios. It is experimentally demonstrated that our method receives 96.6% accuracy in the detection of vehicle status, which is 9% higher than the state-of-the-art method. Finally, a robust parking guidance system gets 97% accuracy.

Innovative Smart Road Stud Sensor Network Development for Real-Time Traffic Monitoring

Intelligent transportation infrastructure has gained significant research attention recently. In this paper, an innovative sensor network of smart road stud (SRS) is developed to enhance traffic detection infrastructure characterized by its functionality in traffic data collection, long/short range wireless data transmission, self-sustained power supply, and remote custom controlled lighting-based traffic guidance. Compared to the traditional traffic detectors and road studs, SRS nodes are installed on lane lines instead of lane center to enable the additional applications besides the detection function, such as traffic monitoring, congestion warning, routing guidance, and so on. SRS detects vehicles based on three-axis geomagnetic sensors. A vehicle detection algorithm is proposed correspondingly under different operation scenarios to count vehicles in two adjacent lanes. Its detecting accuracy can be further improved by a sensor network of multiple SRSs working cooperatively. Field test results show that the vehicle detection accuracy based on the SRS network is about 98% per lane, which is the same level as the commercial detector installed in center of lane, even under the non-standard driving behaviors such as crossing lane line. The high performance, value-added service, and low cost enable wide-range applications of SRS networks as part of intelligent traffic detection infrastructure.