Radar is an important sensor in electromagnetic spectrum warfare. Its confrontation with naval vessels has become increasingly competitive in recent years. However, current radar anti‐jamming methods are limited to some extent in complex electromagnetic environments, which poses a severe challenge to radar's detection and anti‐jamming capabilities. To improve the anti‐jamming capacity of radar, the authors propose a Stackelberg game‐based optimisation method to enhance the decision‐making of anti‐jamming strategies. First, we analyse the radar's winning conditions by considering the temporal constraints of non‐real‐time radar recognition and preparation actions and construct the radar's actual utility matrix. Second, we construct a Stackelberg game‐based model under the condition of a certain recognition probability, and update the recognition probability and recognition interval during the game. Finally, we discuss the conditions under which the radar can obtain a positive benefit in the game and compare the benefit to the reinforcement learning optimisation method. The simulation experimental results show that the proposed strategy can significantly improve the radar's winning probability in the confrontation.

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The confrontation between radar and jammer is increasingly competitive in electromagnetic spectrum warfare. The current radar anti-jamming methods are constrained to some extent in complex electromagnetic environment. To improve the anti-jamming capacity of radar system, a game theory-based optimization method is proposed to enhance the decision-making of anti-jamming strategy in this paper. First, a temporal sequence interaction based dynamic game model between radar and jammer is constructed. Then, Q-learning is performed to optimize the radar anti-jamming strategies. The simulation experimental results show that the proposed method can significantly improve the radar winning probability in the confrontation.

Accurate and timely estimation of cotton yield over large areas is essential for precision agriculture, facilitating the operation of commodity markets and guiding agronomic management practices. Remote sensing (RS) and crop models are effective means to predict cotton yield in the field. The satellite vegetation indices (VIs) can describe crop yield variations over large areas but can’t take the exact environmental impact into consideration. Climate variables (CVs), the result of the influence of spatial heterogeneity in large regions, can provide environmental information for better estimation of cotton yield. In this study, the most important VIs and CVs for estimating county-level cotton yield across Xinjiang Province were screened out. We found that the VIs of canopy structure and chlorophyll contents, and the CVs of moisture, were the most significant factors for cotton growth. For yield estimation, we utilized four approaches: least absolute shrinkage and selection operator regression (LASSO), support vector regression (SVR), random forest regression (RFR) and long short-term memory (LSTM). Due to its ability to capture temporal features over the long term, LSTM performed best, with an R2 of 0.76, root mean square error (RMSE) of 150 kg/ha and relative RMSE (rRMSE) of 8.67%; moreover, an additional 10% of the variance could be explained by adding CVs to the VIs. For the within-season yield estimation using LSTM, predictions made 2 months before harvest were the most accurate (R2 = 0.65, RMSE = 220 kg/ha, rRMSE = 15.97%). Our study demonstrated the feasibility of yield estimation and early prediction at the county level over large cotton cultivation areas by integrating satellite and environmental data.

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