Aiming at the problem that the measured accuracy of the electric field intensity which is affected by the coupling interference by sensor output signal from the component of a three dimensional electric field, the causes of the coupling error was analyzed, and a decoupled calibration method based on support vector regression algorithm for three-dimensional electric field sensor is proposed. The solution of the decoupled calibration matrix was regarded as a multi-objective optimization process, and the optimal decoupling calibration matrix was obtained by the ν-SVR algorithm. The complex inverse calculation of the matrix was avoided, and the calculation error was reduced. A rotary calibration device was designed to accurately measure the angle between the induction electrode of the sensor and the electric-field vector, and an accurate calculation model of the theoretical electric field was established. The experimental results showed that the error between the calculated and theoretical values of the electric-field components obtained by the proposed method were smaller than those obtained by the traditional inverse matrix calibration method, the accuracy of the calibration was improved, the rationality of the calibration method was proven, and the accuracy of the three-dimensional electric-field intensity measurements was further improved.

Aiming at the issues that the field mill type electric field sensor lacks an accurate and complete mathematical model, and its signal is weak and contains a large amount of harmonic noise, on the basis of establishing the mathematical model of the sensor’s induction electrode, the finite element method was adopted to analyze the influence laws of parameters such as the thickness of the shielding electrode and the distance between the induction electrode and the shielding electrode on the sensor sensitivity. On this basis, the above parameters were optimized. A signal processing circuit incorporating a pre-integral transformation circuit, a differential amplification circuit, and a bias circuit was investigated, and a completed mathematical model of the input and output of the field mill type electric field sensor was established. An improved harmonic detection method combining fast Fourier transform and back propagation neural network (FFT-BP) was proposed, the learning rate, momentum factor, and excitation function jointly participated in the adjustment of the network, and the iterative search range of the algorithm was limited by the threshold interval, further improving the accuracy and rapidity of the sensor measurement. Experimental results indicate that within the simulated electric field intensity range of 0–20 kV/m in the laboratory, the measurement resolution of this system can reach 18.7 V/m, and the measurement linearity is more than 99%. The designed system is capable of measuring the atmospheric electric field intensity in real time, providing necessary data support for lightning monitoring and early warning.

Shadow detection helps reduce ambiguity in object detection and tracking. However, existing shadow detection methods tend to misidentify complex shadows and their similar patterns, such as soft shadow regions and shadow‐like regions, since they treat all cases equally, leading to an incomplete structure of the detected shadow regions. To alleviate this issue, we propose a structure‐aware transformer network (STNet) for robust shadow detection. Specifically, we first develop a transformer‐based shadow detection network to learn significant contextual information interactions. To this end, a context‐aware enhancement (CaE) block is also introduced into the backbone to expand the receptive field, thus enhancing semantic interaction. Then, we design an edge‐guided multi‐task learning framework to produce intermediate and main predictions with a rich structure. By fusing these two complementary predictions, we can obtain an edge‐preserving refined shadow map. Finally, we introduce an auxiliary semantic‐aware learning to overcome the interference from complex scenes, which facilitates the model to perceive shadow and non‐shadow regions using a semantic affinity loss. By doing these, we can predict high‐quality shadow maps in different scenarios. Experimental results demonstrate that our method reduces the balance error rate (BER) by 4.53%, 2.54%, and 3.49% compared to state‐of‐the‐art (SOTA) methods on the benchmark datasets SBU, ISTD, and UCF, respectively.

The traditional process of measuring a three-dimensional (3D) electric field in the atmosphere may suffer from limited accuracy of the electric field component intensity due to multiple factors, such as the 3D electric field sensor’s pose change due to the sensor-carrier motion and the non-parallel positions between the 3D electric compass and field sensor’s measurement axes. Thus, this paper proposes a modified 3D electric field directional decomposition algorithm to overcome this problem. Specifically, the developed solution utilizes a parameter identification and fine-tuning scheme that solves the inaccurate measurement problem caused by the non-coaxial 3D positions between the electric field sensor and the compass. The measurement results highlight that the proposed method eliminates the instability of the electric field intensity measurement errors caused by the 3D electric field sensor changes because the measurement accuracy of the corrected electric field component intensity does not change with the sensor’s rotation angle. Our technique reduces the measurement error of the electric field intensity originating from the non-parallel position 3D electronic compass and field sensor measurement axes, further improving the measurement accuracy of the atmospheric field intensity.