In this paper, we present a rigorous numerical approach for the electromagnetic (EM) analyses of a hypersonic object with plasma sheath. First, the effective EM parameters of the plasma flow are constructed by solving the Navier-Stokes fluid dynamics equation combined with proper thermochemical models. Then, the EM characteristics of the hypersonic complex are examined by the method of moments based on the volume-surface integral equations. Both the fluid field properties and the associated EM effects are analyzed for a real sphere-cone object that flies at a hypervelocity of 7650 m/s and at an altitude of 61 or 71 km. The impacts of the plasma sheath on the propagation, scattering, and radiation properties are discussed. The plasma sheath may block the transmission and radiation of radio waves for a wide range of frequencies, as the attenuation may exceed 20 dB. Also, the plasma sheath may influence the backscattering radar cross section to an extent of 20 dB. As the frequency increases, the effects tend to weaken and eventually may be ignored beyond 50 GHz. The simulation results are found in good agreement with available experimental data, which indicates that the proposed approach is reliable for predicting the level of radio blackout.

In this communication, we present a tailored multilevel fast multipole algorithm (MLFMA) for efficient analysis of scattering by multiple moving objects. There are two major issues with the conventional MLFMA for multiple moving targets. One is that the relative positions and attitudes of the targets change with time and we have to re-mesh the solution domain and re-fill the matrix elements at each simulation moment. The other is that we have to solve the matrix equations repeatedly for the moving targets. To alleviate these two burdens, we adopt the stationary grouping scheme in using the MLFMA for individual object, which is unchanged as the object is moving. With the grouping scheme, the near-interactions are invariant, and most far-interactions are reusable when the objects are in motions. The number of levels of MLFMA remains the same even if the overall dimension of the multiple targets is changing. The interactions between objects are expressed via coordinate transforms at the highest levels of their octrees. To accelerate the iterative solutions, the sparse approximate inverse (SAI) preconditioner is incorporated. Numerical results show that the saving of CPU time is substantial for continuous simulations of a maneuvering process of a group of targets.

Novel characteristic mode formulations for penetrable objects based on explicit separation of dissipation power from the total active power and use of single surface integral equation (SSIE) are proposed. Due to the separation of the dissipation power, the right hand side of the generalized eigenvalue equation can be set to be exclusively related to the radiated power, which permits the far field pattern by different characteristic modes to be orthogonal whether the material is lossless or not. The use of single surface integral equation allows both the equivalent electric and magnetic currents to be expressed by a single effective electric or magnetic current, so that the conventional inverse to express the equivalent electric current by the equivalent magnetic current or vice versa is avoided, which is favorable for frequency sweeping simulations. Numerical examples for verifications are provided by comparing with acknowledged results or measured data.

A numerical procedure for analysis of electromagnetic scattering by a hypersonic cone-like body flying in the near space is presented. First, the fluid dynamics equation is numerically solved to obtain the electron density, colliding frequency, and the air temperature around the body. They are used to calculate the complex relative dielectric constants of the plasma sheath. Then the volume-surface integral equation method is adopted to analyze the scattering properties of the body plus the plasma sheath. The Backscattering Radar Cross-Sections (BRCS) for the body flying at different speeds, attack angles, and elevations are examined. Numerical results show that the BRCS at a frequency higher than 300 MHz is only slightly affected if the speed is smaller than 7 Mach. The BRCS at 1 GHz would be significantly reduced if the speed is greater than 7 Mach and is continuously increased, which can be attributed to the absorption by the lossy plasma sheath. Typically, the BRCS is influenced by 5~10 dBm for a change of attack angle within 0~15 degrees, or for a change of elevation within 30~70 km above the ground.

In this letter, an appropriate approach is proposed for modeling the electromagnetic (EM) scattering from composite rough surfaces made up of wake due to a submerged body and wind-driven sea waves. The computational fluid dynamics (CFD) method is used to extract the air–seawater surface wake generated by an underwater moving body at different speeds and depths. Then, the wake is superimposed on the randomly rough wind-driven sea surfaces that obey the Pierson–Moskowitz power spectrum. The small slope approximation (SSA) method is adopted to calculate the EM scattering by the composite surfaces. The simulation results are obtained and justified.

The hyperspectral infrared observations of the Geostationary Interferometric Infrared Sounder (GIIRS) on the Fengyun-4A (FY-4A) satellite are an important data source for numerical weather prediction (NWP) assimilation. However, there are systematic differences between observed and simulated brightness temperatures (i.e., the observation increments contain predictable systematic bias components). To address the issue that traditional linear methods struggle to capture the nonlinear relationships between biases and forecast predictors, this study proposes an intelligent bias correction method that integrates ensemble learning and explainable artificial intelligence. First, the entropy reduction method is used to select 69 mid-wave channels. Then, Random Forest, XGBoost, LightGBM, Decision Tree, and Extra Tree are used as base learners to construct a weighted average ensemble model. Training and validation are conducted using high-frequency clear-sky observation data from FY-4A/GIIRS during Typhoon Lekima. The results show that: (1) the ensemble learning correction method outperforms single models and traditional offline methods, with root mean square errors of brightness temperature bias of less than 0.9209 K for the training set and 1.4447 K for the test set; (2) Shapley Additive Explanations (SHAP)-based interpretability analysis reveals the contribution and nonlinear influence mechanisms of factors such as longitude, atmospheric thickness, surface temperature, and total precipitable water on bias correction. This study provides an intelligent bias correction framework with both high precision and explainability, offering a reference for the bias correction and assimilation applications of hyperspectral satellite observations like GIIRS.