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The total electron content (TEC) of the ionosphere is an important parameter to describe the ionosphere, and it is a great significance to monitor and predict it accurately. In this paper, a hybrid ionospheric TEC prediction model based on the least squares support vector machine (LSSVM) and the Moth-Flame Optimization (MFO) algorithm is proposed. The parameters of the LSSVM model are optimized by the MFO algorithm. We use observation data of 15 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to extract ionospheric TEC from 2012 to 2019. The ionospheric TEC is forecasted using solar and geomagnetic activity indices in both the low solar activity year (2019) and the high solar activity year (2015). The results show that the prediction performance of the MFO_LSSVM model is significantly better than that of the IRI model, SVM model, and LSSVM model. Compared with the other three models, there are more stable prediction results in the low and high solar activity years. At the same time, the predicted value of the MFO_LSSVM model has a good correlation with the measured value, and it also has good prediction potential in areas with active geomagnetic activity. The comparison with the Long Short-Term Memory (LSTM) model shows that the MFO_LSSVM model has better performance than the single LSTM model. In conclusion, the MFO_LSSVM model can accurately predict ionospheric TEC in China, and has better accuracy than traditional long-term and short-term models. [ABSTRACT FROM AUTHOR]

A robust ionospheric model is indispensable for providing the atmospheric delay corrections for global navigation satellite system (GNSS) navigation and positioning and forecasting the space environment. The accuracy of ionospheric models is limited due to the simplified model structures. Complicated spatiotemporal variations in total electron content (TEC) biases between GNSS and international reference ionosphere (IRI) suggest a robust strategy to optimally combine GNSS and IRI TEC for high-precision modeling. In this paper, we propose a novel ionospheric data assimilation method, which is a local ensemble transform Kalman filter (LETKF), to construct an ionospheric model over Yunnan in southwestern China. We used the LETKF method to assimilate the ionospheric TEC extracted from GNSS observations in Yunnan into the IRI-2016 model. The experimental results indicate that the ionospheric data assimilation has a more pronounced improvement effect on the IRI empirical model during periods of geomagnetic quiet than during periods of geomagnetic disturbance. On quiet magnetic days, the skill score (SKS) of the assimilation is 0.60 and the root mean square error (RMSE) values before and after assimilation are 5.08 TECU and 2.02 TECU, respectively. The correlation coefficient after assimilation increases from 0.94 to 0.99. On magnetic storm days, the SKS of the assimilation is 0.42 and the RMSE values before and after assimilation are 5.99 TECU and 3.46 TECU, respectively. The correlation coefficient after assimilation increases from 0.98 to 0.99. The results suggest that the LETKF algorithm can be considered an effective method for ionospheric data assimilation. [ABSTRACT FROM AUTHOR]

Ionospheric total electron content (TEC) is important data for ionospheric morphology, and also an important parameter for ionospheric correction in Global Navigation Satellite System (GNSS) precise positioning, navigation, and radio science. In this study, we present a data assimilation model for regional ionosphere based on a local ensemble Kalman filter (LEnKF) with the International Reference Ionosphere 2016 (IRI-2016) model as the background, to assimilate ionospheric TEC observations from GNSS. To demonstrate the results, the TEC estimates from the Crustal Movement Observation Network of China (CMONOC), which is about 260 stations in China, are applied as observation. The assessments are performed against the TEC estimates from BeiDou Navigation Satellite System (BDS) geostationary earth orbit (GEO) and against the final products from the Center for Orbit Determination in Europe (CODE). The assimilation results are in good agreement with BDS GEO TEC and the CODE TEC on a quiet or disturbed day. The correlation coefficient after assimilation is increased by about 17% compared with that before assimilation, and the RMSE after assimilation is decreased by about 42% compared with that before assimilation. Furthermore, the assimilated method is also evaluated in the single-frequency precise point positioning (PPP). The experimental results indicate that the PPP/Assimilated method can improve the GNSS positioning accuracy more effectively in comparison to the PPP/CODE. These results reveal that the LEnKF method can be considered as a useful tool for ionospheric assimilation. [ABSTRACT FROM AUTHOR]

Ionospheric forecasts are critical for space-weather anomaly detection. Forecasting ionospheric total electron content (TEC) from the global navigation satellite system (GNSS) is of great significance to near-earth space environment monitoring. In this study, we propose a novel ionospheric TEC forecasting model based on deep learning, which consists of a convolutional neural network (CNN), long-short term memory (LSTM) neural network, and attention mechanism. The attention mechanism is added to the pooling layer and the fully connected layer to assign weights to improve the model. We use observation data from 24 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to model and forecast ionospheric TEC. We drive the model with six parameters of the TEC time series, Bz, Kp, Dst, and F10.7 indices and hour of day (HD). The new model is compared with the empirical model and the traditional neural network model. Experimental results show the CNN-LSTM-Attention neural network model performs well when compared to NeQuick, LSTM, and CNN-LSTM forecast models with a root mean square error (RMSE) and R2 of 1.87 TECU and 0.90, respectively. The accuracy and correlation of the prediction results remained stable in different months and under different geomagnetic conditions. [ABSTRACT FROM AUTHOR]

Accurate corrections for ionospheric total electron content (TEC) and early warning information are crucial for global navigation satellite system (GNSS) applications under the influence of space weather. In this study, we propose to use a new machine learning model—the Prophet model, to predict the global ionospheric TEC by establishing a short-term ionospheric prediction model. We use 15th-order spherical harmonic coefficients provided by the Center for Orbit Determination in Europe (CODE) as the training data set. Historical spherical harmonic coefficient data from 7 days, 15 days, and 30 days are used as the training set to model and predict 256 spherical harmonic coefficients. We use the predicted coefficients to generate a global ionospheric TEC forecast map based on the spherical harmonic function model and select a year with low solar activity (63.4 < F10.7 < 81.8) and a year with the high solar activity (79.5 < F10.7 < 255.0) to carry out a sliding 2-day forecast experiment. Meanwhile, we verify the model performance by comparing the forecasting results with the CODE forecast product (COPG) and final product (CODG). The results show that we obtain the best predictions by using 15 days of historical data as the training set. Compared with the results of CODE'S 1-Day (C1PG) and CODE'S 2-Day (C2PG). The number of days with RMSE better than COPG on the first and second day of the low-solar-activity year is 151 and 158 days, respectively. This statistic for high-solar-activity year is 183 days and 135 days. [ABSTRACT FROM AUTHOR]

Computerized ionospheric tomography is a highly ill-posed inverse problem, and regularization tends to stabilize the problem to provide a unique solution. When a regularization method is used, the choice of an optimal parameter is a key issue. In this article, we propose an adaptive regularization method for 3-D ionospheric tomography based on the U-curve. The proposed approach uses a U-curve method to determine the optimal regularization parameter from Global Navigation Satellite Systems (GNSS) observation data. Comparative case studies are investigated based on GNSS simulated observations and real measurements. The simulation results indicate that the proposed method is superior to the adaptive regularization method based on the L-curve. In addition, we further validate the tomographic results with actual ionosonde station data. The results demonstrate the reliability and superiority of the proposed method compared to traditional methods. [ABSTRACT FROM AUTHOR]

The total electron content (TEC) is a critical parameter of ionosphere morphology. Precise modeling and prediction of the TEC time series can significantly aid the functioning of the Global Navigation Satellite System (GNSS), satellite as well as shortwave communications. An improved Transformer-based TEC prediction model (ITM) is proposed in this study, in response to the high noise characteristics of the temporal data for TEC in the global ionosphere. The ITM model forecasts the global ionospheric TEC data provided by the Center for Orbit Determination in Europe (CODE) for the year 2018. Experimental results indicate that the model obtains an annual average root mean square error of 1.4 TECU, which represents a 6.67 % advancement over the daily forecast product C1PG published by the CODE center. The model also attains an annual average mean absolute error of 1 TECU, representing a 9.09 % improvement over the accuracy of C1PG. The annual average correlation coefficient is 0.98, which indicates a 0.62 % upgrade in C1PG's accuracy. Additionally, the model exhibits a maximum increase of 23.53 % in monthly average root mean square error over C1PG, a maximum increase of 25 % in monthly average mean absolute error, and a maximum increase of 1.55 % in monthly average correlation coefficient. During the more geomagnetically active phases of 2018, the ITM model's forecast accuracy exhibited an overall improvement of 5 % in RMSE, 7.14 % in MAE, and 0.83 % in correlation coefficient R compared to the C1PG product. [ABSTRACT FROM AUTHOR]

Landslides are natural phenomena, causing serious fatalities and negative impacts on socioeconomic. The Three Gorges Reservoir (TGR) area of China is characterized by more prone to landslides for the rainfall and variation of reservoir level. Prediction of landslide displacement is favorable for the establishment of early geohazard warning system. Conventional machine learning methods as forecasting models often suffer gradient disappearance and explosion, or training is slow. Hence, a dynamic method for displacement prediction of the step-wise landslide is provided, which is based on gated recurrent unit (GRU) model with time series analysis. The establishment process of this method is interpreted and applied to Erdaohe landslide induced by multi-factors in TGR area: the accumulative displacements of landslide are obtained by the global positioning system; the measured accumulative displacements is decomposed into the trend and periodic displacements by moving average method; the predictive trend displacement is fitted by a cubic polynomial; and the periodic displacement is obtained by the GRU model training. And the support vector machine (SVM) model and GRU model are used as comparisons. It is verified that the proposed method can quite accurately predict the displacement of the landslide, which benefits for effective early geological hazards warning system. Moreover, the proposed method has higher prediction accuracy than the SVM model. [ABSTRACT FROM AUTHOR]

To improve the seamless navigation ability of an integrated Global Positioning System (GPS)/inertial navigation system in GPS-denied environments, a hybrid navigation strategy called the self-learning square-root- cubature Kalman filter (SL-SRCKF) is proposed in this article. The SL-SRCKF process contains two innovative steps: 1) it provides the traditional SRCKF with a self-learning ability, which means that navigation system observations can be provided continuously, even during long-term GPS outages; and 2) the relationship between the current Kalman filter gains and the optimal estimation error is established, which means that the optimal estimation accuracy can be improved by error compensation. The superiority of the proposed SL-SRCKF strategy is verified via experimental results and prominent advantages of this approach include: 1) the SL-SRCKF comprises two cycle filtering systems that work in a tightly coupled mode, and this allows more accurate error correction results to be obtained during GPS outages; 2) the system's error prediction ability is effectively improved by introducing a long short-term memory, which provides much better performance than other neural networks, such as random forest regression or the recursive neural network; and 3) under different (30, 60, and 100 s) GPS outage conditions, the long-term stability of SL-SRCKF is much better than that of other error correction approaches. [ABSTRACT FROM AUTHOR]

In this paper, a seamless navigation dual-model based on Square-Root Cubature Kalman Filter (SRCKF) and Random Forest Regression (RFR) is developed to enhance the performance of the Global Positioning System (GPS)/Inertial Navigation System (INS) integrated navigation system. By using the proposed method, the system can ensure seamless navigation ability even during GPS signal outages. In the proposed dual-model, sub-model 1 that directly relates the specific force of INS to the measurement of filter and sub-model 2 that directly relates the cubature points and innovation of SRCKF to the error caused by filter are established. Combined with SRCKF and RFR algorithms, the dual-model system can predict and estimate the velocity and position of the vehicle seamlessly when GPS signals are blocked. Field test data are collected to evaluate the proposed solution, and the experimental results show that the model proposed has obvious improvement in navigation accuracy by comparison. The prominent advantages of the proposed seamless navigation method include the following: (i) the proposed dual-model can effectively provide corrections to standalone INS during GPS outages, which outperforms traditional widely used single model; (ii) the proposed combination of SRCKF and RFR achieves better performance in the prediction of INS errors than other combination algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a global positioning method based on a polarized light compass system. A main limitation of polarization positioning is the environment such as weak and locally destroyed polarization environments, and the solution to the positioning problem is given in this paper which is polarization image de-noising and segmentation. Therefore, the pulse coupled neural network is employed for enhancing positioning performance. The prominent advantages of the present positioning technique are as follows: (i) compared to the existing position method based on polarized light, better sun tracking accuracy can be achieved and (ii) the robustness and accuracy of positioning under weak and locally destroyed polarization environments, such as cloudy or building shielding, are improved significantly. Finally, some field experiments are given to demonstrate the effectiveness and applicability of the proposed global positioning technique. The experiments have shown that our proposed method outperforms the conventional polarization positioning method, the real time longitude and latitude with accuracy up to 0.0461° and 0.0911°, respectively. [ABSTRACT FROM AUTHOR]

Ionospheric disturbances constitute the main restriction factor for precise positioning techniques based on global positioning system (GPS) measurements. Simultaneously, GPS observations are widely used to determine ionospheric disturbances with total electron content (TEC). In this paper, we present an analysis of ionospheric disturbances over China mid- and low-latitude area before and during the magnetic storm on 17 March 2015. The work analyses the variation of magnetic indices, the amplitude of ionospheric irregularities observed with four arrays of GPS stations and the influence of geomagnetic storm on GPS positioning. The results show that significant ionospheric TEC disturbances occurred between 10:30 and 12:00 UT during the main phase of the large storm, and the static position reliability for this period are little affected by these disturbances. It is observed that the positive and negative disturbances propagate southward along the meridian from mid-latitude to low-latitude regions. The propagation velocity is from about 200 to 700ms-1 and the amplitude of ionospheric disturbances is from about 0.2 to 0.9 TECU min-1. Moreover, the position dilution of precession (PDOP) with static precise point positioning (PPP) on storm and quiet days is 1.8 and 0.9 cm, respectively. This study is based on the analysis of ionospheric variability with differential rate of vertical TEC (DROVT) and impact of ionospheric storm on positioning with technique of GPS PPP. [ABSTRACT FROM AUTHOR]

Ionospheric anomalies possibly associated with large earthquakes, particularly coseismic ionospheric disturbances, have been detected by global positioning system (GPS). A large Nepal earthquake with magnitude Mw 7.8 occurred on April 25, 2015. In this paper, we investigate the multi-dimensional distribution of near-field coseismic ionospheric disturbances (CIDs) using total electron content (TEC) and computerized ionospheric tomography (CIT) from regional GPS observational data. The results show significant ionospheric TEC disturbances and interesting multi-dimensional structures around the main shock. Regarding the TEC changes, coseismic ionospheric disturbances occur approximately 10–20 min after the earthquake northeast and northwest of epicentre. The maximum ridge-to-trough amplitude of CIDs is up to approximately 0.90 TECU/min. Propagation velocities of the TEC disturbances are 1.27 ± 0.06 km/s and 1.91 ± 0.38 km/s. It is believed that the ionospheric disturbances are triggered by acoustic and Rayleigh waves. Tomographic results show that the three-dimensional distribution of ionospheric disturbances obviously increases at an altitude of 300 km above the surrounding epicentre, predominantly in the entire region between 200 km and 400 km. Significant ionospheric disturbances appear at 06:30 UT from tomographic images. This study reveals characteristics of an ionospheric anomaly caused by the Nepal earthquake. [ABSTRACT FROM AUTHOR]

• An orientation method with limited effective polarization area is proposed. • Using UNet to separate occluders and effective polarization regions. • Solar Meridian Fitting Using Antisymmetry of Skylight Polarization Pattern. The bionic polarized light compass has characteristics of immunity to interference from electromagnetic fields and zero error accumulation over time, and thus has good application prospects in the autonomous navigation field under conditions of Global Positioning System rejection or failure. With the aim of addressing the problem that the polarization orientation accuracy of the bionic polarized light compass can be degraded seriously by the presence of large-area occlusions such as clouds and buildings during the actual application process, this paper proposes a highly robust orientation method under the limited effective polarization area condition based on the antisymmetry of the skylight polarization pattern. The orientation algorithm first uses the UNet convolutional neural network to perform binary segmentation of the polarized image and then separates and removes the thick cloud layer/building occlusion area from the image; Then, a new solar meridian fitting method is proposed, which makes use of the characteristic of antisymmetric distribution of polarization angle along the solar meridian in atmospheric polarization mode to fit the solar meridian, and then completes the orientation. Experimental results show that the method proposed in this paper can effectively solve the problem of polarization orientation accuracy degradation caused by harsh environments, including clouds, building occlusions, and cloudy days, and can thus improve the robustness of bionic polarized light compass orientation effectively in complex environments. [ABSTRACT FROM AUTHOR]

A moderate geomagnetic storm occurred on March 17, 2013, during which large-scale traveling ionospheric disturbances (LSTIDs) are observed over China by ionosondes and GPS from Crustal Movement Observation Network of China (CMONOC) and the International GNSS Service (IGS). Ionosonde data and computerized ionospheric tomography (CIT) technique are employed to analyze the disturbances in our study. The maximum entropy cross spectral analysis (MECSA) method is used to obtain the propagation parameters of the LSTIDs. Spatio-temporal variations of ionospheric electron density (IED) and total electron content (TEC) during this geomagnetic storm over China are investigated. Disturbance images of IED and TEC are also presented in the paper. The results show two LSTID events at about 12:00 UT and 15:00 UT during the main phase of the storm. Besides, the LSTIDs with a duration of 40 min are detected over China. It is confirmed that the LSTIDs travel from north to south with a horizontal velocity of 400–500 m/s, and moved southwestwards with a horizontal velocity of 250–300 m/s, respectively. [ABSTRACT FROM AUTHOR]

The drift of inertial navigation system (INS) will lead to large navigation error when a low-cost INS is used in microaerial vehicles (MAV). To overcome the above problem, an INS/optical flow/magnetometer integrated navigation scheme is proposed for GPS-denied environment in this paper. The scheme, which is based on extended Kalman filter, combines INS and optical flow information to estimate the velocity and position of MAV. The gyro, accelerator, and magnetometer information are fused together to estimate the MAV attitude when the MAV is at static state or uniformly moving state; and the gyro only is used to estimate the MAV attitude when the MAV is accelerating or decelerating. The MAV flight data is used to verify the proposed integrated navigation scheme, and the verification results show that the proposed scheme can effectively reduce the errors of navigation parameters and improve navigation precision. [ABSTRACT FROM AUTHOR]

Abstract: Reconstructing ionospheric electron density (IED) is an ill-posed inverse problem, with classical Tikhonov regularization tending to smooth IED structures. By contrast, total variation (TV) regularization effectively resists noise and preserves discontinuities of the IED. In this paper, we regularize the inverse problem by incorporating both Tikhonov and TV regularization. A specific formulation of the proposed method, called hybrid regularization, is introduced and investigated. The method is then tested using simulated data for the actual positions of the GPS satellites and ground receivers, and also applied to the analysis of real observation data under quiescent and disturbed ionospheric conditions. Experiments demonstrate the effectiveness, and illustrate the validity and reliability of the proposed method. [Copyright &y& Elsevier]

The insufficiency of data is the essential reason for ill-posed problem existed in computerized ionospheric tomography (CIT) technique. Therefore, the method of integrating multi-source data is proposed. Currently, the multiple satellite navigation systems and various ionospheric observing instruments provide abundant data which can be employed to reconstruct ionospheric electron density (IED). In order to improve the vertical resolution of IED, we do research on IED reconstruction by integration of ground-based GPS data, occultation data from the LEO satellite, satellite altimetry data from Jason-1 and Jason-2 and ionosonde data. We used the CIT results to compare with incoherent scatter radar (ISR) observations, and found that the multi-source data fusion was effective and reliable to reconstruct electron density, showing its superiority than CIT with GPS data alone. [ABSTRACT FROM AUTHOR]

• A dual-model instead of traditional single model comprised of CKF-MLP and RBF-CKF algorithms is proposed for position and velocity error compensation during GPS outages. • CKF is added in MLP to reduce network errors and improve modelling accuracy by estimating the weights of MLP adaptively for training process which is termed as CKF-MLP algorithm. • RBF is added in CKF to predict the errors caused by filter itself and improve INS error compensation accuracy which is termed as RBF-CKF algorithm. To improve the performance of a microelectromechanical-system (MEMS)-based inertial navigation system (INS)/Global Positioning System (GPS) integrated navigation system, a dual optimization scheme comprising a cubature Kalman filter (CKF)-multiple layer perceptron (MLP) and radial basis function (RBF)-CKF is proposed for the compensation of position and velocity errors during GPS outages. The proposed method has advantages: (i) The generalization ability of the CKF-MLP is much better than that of other methods, such as the extended Kalman filter (EKF)-MLP and unscented Kalman filter (UKF)-MLP, and the proposed CKF-MLP provides highly accurate position information even during long GPS outages; (ii) The accuracy of the error estimation of the RBF-CKF is higher than that of other neural-network-assisted CKF methods, such as the Adaboost-CKF and random forest (RF)-CKF, and the proposed CKF-MLP can estimate the weights of the MLP adaptively and achieve an appropriate internal structure when the GPS signal is available; (iii) The RBF is added to the CKF to establish the relationship between filter parameters and estimation error in the proposed RBF-CKF; (iv) During a GPS outage, position errors and velocity errors are predicted and compensated for in the proposed dual optimization scheme. Field test data are collected to evaluate the proposed solution. Experimental results show that (i) the root-mean-square error of the velocity for an eastern position is reduced by 77% to 0.34 m/s using the proposed CKF-MLP strategy; (ii) the root-mean-square error of a northern position determined by the proposed RBF-CKF remains at 23.11 m even during long outages of 500 s, and the RBF-CKF effectively suppresses the divergence seen for other methods; and (iii) the dual optimization process using different estimators provides better error compensation results than a single optimization method, which demonstrates that the proposed solution leads to the better performance of a MEMS-based INS/GPS navigation system. [ABSTRACT FROM AUTHOR]