Xiaoyu Ma

Owing to the integration of numerous electronic devices into flight platforms, the resulting complex magnetic interference environment significantly impacts geomagnetic field measurement accuracy. To improve the compensation accuracy of traditional models and enhance their ability to characterize non-maneuvering interference, this study proposes a dual-channel hybrid-driven aeromagnetic compensation method incorporating time–frequency features. First, a model-data hybrid-driven approach is introduced to model different types of magnetic interference based on the classification of aeromagnetic interference. Additionally, the V-Coherence method is employed to select current signals from onboard equipment that are highly correlated with magnetic interference. Second, to further enrich feature representation, wavelet packet decomposition is introduced to expand the time–frequency characteristics of network inputs, and a dual-channel network is constructed to achieve effective compensation for complex magnetic interference from the aircraft platform. Finally, based on the proposed model, a cross-training strategy for calibrated flight data is explored. The proposed method is validated using field magnetic survey data in a public dataset. The results show that the proposed method achieves superior performance across all test regions compared to traditional approaches, effectively improves the accuracy of geomagnetic field measurements.

In recent years, research has shown that geomagnetic navigation technology can be an important alternative to satellite navigation for the navigation and positioning of aircraft. Due to the limited information contained in the scalar geomagnetic field, studying the application of geomagnetic vector fields is significant for the development of geomagnetic navigation technology. In aerial navigation and positioning, the attitude error of the aircraft obtained from the inertial navigation system (INS) will continuously diverge, which seriously affects the measurement accuracy of the geomagnetic vector field. To address this issue, we extended the classic aeromagnetic interference model (Tolles–Lawson model) to its vector form and established a joint estimation model for the geomagnetic vector and inertial navigation errors. A marginalized particle filter is designed to solve the problem of large state variable dimensions and complex calculations. In the joint estimation model, we provided a detailed description of the relationship between the measured magnetic field and the variation of the geomagnetic vector field, as well as the errors in the INS. The proposed algorithm is validated using a geomagnetic/INS semi-physical navigation experimental system, which can fully reproduce the influences of various environmental factors on measurement data during real aircraft motion. The simulation and experimental results show that our estimation algorithm can effectively reduce the impact of INS attitude errors on the geomagnetic vector field measurement results, while suppressing the divergence of magnetic field errors, indicating that geomagnetic measurements and applications of geomagnetic vector fields are promising directions for geomagnetic navigation technology development.

Aeromagnetic interference is one of the important factors limiting the application of aeromagnetic data on aircraft platforms. Therefore, magnetic compensation is necessary for aeromagnetic data processing, which is of great significance to improve the accuracy of geomagnetic navigation. In recent years, aeromagnetic compensation methods can be mainly divided into two categories: linear regression methods based on the Tolles-Lawson (T-L) model and data-driven methods based on machine learning. However, the accuracy of linear regression methods is subject to the complexity of the model and the problem of multicollinearity, while data-driven methods require the quantity and quality of aeromagnetic measurement data. To solve this problem, we proposed an aeromagnetic compensation method taking advantage of both the T-L model and neural network. The T-L model parameters are trained through our network, while the attention mechanism is applied in the hidden layer to enhance the feature extraction ability of the model for time series. We validate our method by applying it to an open-access dataset. The Experimental results demonstrate that our method has higher compensation accuracy and generalization performance than the classical algorithms.

Geomagnetic matching navigation technology, as an important assisted navigation method, has gradually been wildly used in the navigation field, in which the quality of geomagnetic reference map is of great significance on improving the guidance and positioning accuracy. However, the existing geomagnetic reference map reconstruction methods can hardly satisfy the actual needs. Considering the popularity of deep learning methods in the image super-resolution (SR) field, a geomagnetic reference map SR method using a convolution neural network is proposed. First, rectangular harmonic analysis theory is used to construct the training dataset, which is based on data from the United States Geological Survey and the International Geomagnetic Reference Field. Then, a multi-channel convolutional neural network, which integrates all the three independent components in the geomagnetic reference field, is proposed of reference map SR reconstruction. Experimental results show the effectiveness of our proposed method over other state-of-the-art methods.

During maneuvering of high-speed aircraft, the eddy-current magnetic field has a significant impact on the real-time geomagnetic field measurement accuracy. However, it is difficult to estimate the eddy-current component directly when it is considered in the magnetic distortion model. In this paper, we proposed a geomagnetic field estimation method based on a shaping filter. Soft-iron, hard-iron and eddy-current field distortions were considered in the compensation system. In the estimation process, a higher-order shaping filter is designed to coincide the higher-order statistics of the alternative system with the input of the original system. Simulation results showed that our proposed method could accurately estimate the eddy-current field. Compared with methods that do not consider the eddy-current field, the root-mean-square error of the geomagnetic field could be reduced by more than 50%, thus significantly improving the accuracy of geomagnetic field measurement.

Aiming at the problems of slow iteration speed, disorder in initial exploration and poor ability to approach the optimal solution of ant colony algorithm, an improved ant colony algorithm based on global view is proposed. Firstly, a multi-level parallel structure with global view is proposed to solve the problem that ant colony algorithm can't find the optimal step size due to local view. The convergence speed and the ability to approximate the optimal solution of ant colony algorithm are improved by parallel structure. Then, a pheromone initialization method and an improved pheromone update method are proposed to improve the initial search disorder of traditional ant colony algorithm. Finally, the local search method based on geometric algorithm and genetic operator is used to alleviate the local optimal solution problem and improve the convergence speed of ant colony algorithm. The experimental results show that the improved ant colony algorithm based on the global view has a significant improvement in the ability of approaching the optimal solution and improving the convergence speed. In the two simulation maps testing, the average path length is optimized by 10.5%, the average number of iterations and the average running time are improved by 73.5% and 81% respectively.

Introduction: Endovascular therapy is the standard of care for severe acute ischemic stroke caused by large‐vessel occlusion in the anterior circulation, but there is a debate on the optimal anesthetic approach during this therapy. Meta‐analyses of observational studies suggest that general anesthesia increases disability and death compared with conscious sedation However, their results are conflicting. This meta‐analysis study was performed to assess the relationship between the effects of general anesthesia compared to conscious sedation during endovascular therapy for acute ischemic stroke.Methods: Through a systematic literature search up to August 2020, 18 studies included 4,802 subjects at baseline with endovascular therapy for acute ischemic stroke and reported a total of 1,711 subjects using general anesthesia and 1,961 subjects using conscious sedation were found. They recorded relationships between the effects of general anesthesia compared to conscious sedation during endovascular therapy for acute ischemic stroke. Odds ratio (OR) or Mean differences (MD) with 95% confidence intervals (CIs) were calculated between the effect of general anesthesia compared to conscious sedation during endovascular therapy for acute ischemic stroke using the dichotomous or contentious methods with a random or fixed‐effect model.Results: No significant difference were found between general anesthesia and conscious sedation during the endovascular therapy for acute ischemic stroke in functional independence at 90 days (OR, 0.78; 95% CI, 0.44–1.40, p = 40); successful recanalization at 24 hr (OR, 1.23; 95% CI, 0.62–2.41, p = 55); mortality at 90 days (OR, 1.36; 95% CI, 0.83–2.24, p = .22); interventional complication (OR, 1.24; 95% CI, 0.76–2.02, p = .40); symptomatic intracranial hemorrhage (OR, 0.64; 95% CI, 0.41–0.99, p = .05); aspiration pneumonia (OR, 0.96; 95% CI, 0.58–1.58, p = .87); and National Institute of Health Stroke Scale score after 24 hr (MD, 0.38; 95% CI, −1.15–1.91, p = .62); with relative relationship favoring general anesthesia only in decreasing the symptomatic intracranial hemorrhage.Conclusions: General anesthesia has no independent relationship compared to conscious sedation during the endovascular therapy for acute ischemic stroke with a relative relationship favoring general anesthesia only in decreasing the symptomatic intracranial hemorrhage.This relationship encouraged us to recommend either anesthetic strategy during the endovascular therapy for acute ischemic stroke with no possible fear of higher complication.

Geomagnetic matching navigation plays a vital role in the navigation and guidance field, and the construction of geomagnetic reference maps plays a significant role in geomagnetic matching navigation. This paper addresses the problem of generating high-resolution geomagnetic reference maps with limited measured data. Most existing methods can hardly satisfy the accuracy requirements. We base our study on the theory of image super-resolution reconstruction and approach this problem from the perspective of dictionary learning and sparse representation. We propose a method of sparse dictionary initialization based on prior information from rectangular harmonic analysis, and then the K-SVD algorithm is applied to the dictionary training process to improve the performance of the sparse dictionary. Three components of the geomagnetic field are considered for multi-channel sparse representation to enhance the quality of the constructed maps. Experimental results show that our proposed method outperforms other geomagnetic reference map construction methods in the reconstructed precision as well as the robustness to noise.