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Group targets such as birds and unmanned aerial vehicles (UAVs) are the research hotspot in the radar field recently. Group target reconstruction that estimates the positions of individual targets becomes one of the core research requirements. However, group targets are usually closely spaced with a similar velocity and it is hard to distinguish multiple targets, resulting in an angular glint. Meanwhile, they usually have weak echoes and fly at a low altitude, which leads to lots of missed detections and false alarms. These characters increase the difficulty of group target reconstruction. Compared with a single radar, a multiradar system with complementary multiview observation can improve the accuracy and completeness of reconstruction. However, a large number of missed detections and false alarms make it difficult to correctly associate multisensor measurements. Moreover, large sensor bias further aggravates the difficulty. This article proposed a topology-feature-aided joint association and registration algorithm to simultaneously acquire association and bias estimation results. The topology feature is applied in the association step to improve association correctness. A nonlinear least median of squares estimator is proposed in the registration step to improve the accuracy and robustness of bias estimation. Multiframe information is used to further improve the association and registration performance. Simulation and experiment results show better bias estimation and association performance of the proposed method compared with existing algorithms and better reconstruction performance compared with a single radar.

With the ever-growing integration of renewable energy sources (RESs) into the power grid to meet escalating power demand, the intermittent and volatile nature of these sources poses significant challenges to the stability of power grid. To address the unstable output power resulting from the inherent randomness and fluctuation of RES, this paper introduces a novel cooperative control strategy designed for a photovoltaic-based grid-connected system. This proposed strategy leverages both battery energy storage system (BESS) and superconducting magnetic energy storage (SMES) within the hybrid energy storage system (HESS) framework. At top-level control (TLC), the control strategy employs a fuzzy control-based low-pass filter (LPF) to dynamically regulate filtration coefficient and realize transient power allocation optimization. Simultaneously, at the underlayer level control (ULC), a fast Model Predictive Control (MPC) method is implemented to maintain DC bus voltage by one-step prediction horizon. The feasibility and superiority of the proposed control strategy are systematically validated under diverse operating conditions, demonstrating its efficacy in enhancing the stability and performance of the grid-connected system.

Predicting aerial animal migration is of great significance for biological research, ecological conservation, and agricultural production. The mechanism of animal migration is deeply coupled with spatiotemporal and meteorological factors. However, the existing large-scale prediction models using weather radar isolate the spatiotemporal characteristics and the meteorological factors. Additionally, their long-term prediction capabilities are limited, posing challenges in accurately forecasting long-term migration patterns to support applications, such as ecological warnings. This article introduces an aerial migration prediction neural network model combining multiple meteorological factors with weather radar data while expanding the horizon of the migration forecast to the scale of 7 days. Differentiated feature extraction methods are applied to different meteorological factors in the network. The transfer characteristics of the wind field in 2-D space are used to construct a dynamic migration model. The scalar meteorological data are encoded by entity embedding to perform feature fusion with the dynamic branch, collectively forming the forecast model that outputs future migration intensity. We validate the effectiveness of our model China weather radar network real data and reanalysis data, accurately forecasting migratory biomass within China for a horizon of up to 7 days. Moreover, our model is compared with two existing prediction models, demonstrating a maximum improvement of 14.00% in the coefficient of determination ( $R^{2}$ ) in long-term forecast, and the visualized results highlight the predictive effectiveness for the spring and autumn seasons. In future applications, more meteorological factors should be considered and radar data from more stations should be collected to enhance the dataset.