To improve the search efficiency of the unmanned aerial vehicle (UAV) swarm in disaster areas, the target distribution probability graph in the prior information is introduced, and a drone cluster search trajectory planning method based on probability of containment (POC) is proposed. Firstly, based on the concept of probability of containment in search theory, a task area division method for polygonal and circular areas is constructed, and the corresponding search trajectory is constructed. Then, the influence of factors, including probability of containment, probability of detection, and probability of success on search efficiency, is sorted out, and the objective function of search trajectory optimization is constructed. Subsequently, an adaptive mutation operator is used to improve the differential evolution algorithm, thus constructing a trajectory optimization process based on the improved adaptive differential evolution algorithm. Through simulation verification, the proposed method can achieve a full coverage search of the task area and a rapid search within a limited time, and can prioritize the coverage of areas with a high target existence probability as much as possible to achieve a higher cumulative success probability. Moreover, the time efficiency and accuracy of the solution are high.

To weaken or avoid the impact of dynamic threats such as wind and extreme weather on the real-time path of a UAV swarm, a path-planning method based on improved long short-term memory (LSTM) network prediction parameters was constructed. First, models were constructed for wind, static threats, and dynamic threats during the flight of the drone. Then, it was found that atmospheric parameters are typical time series data with spatial correlation. The LSTM network was optimized and used to process time series parameters to construct a network for predicting atmospheric parameters. The state of the drone was adjusted in real time based on the prediction results to mitigate the impact of wind or avoid the threat of extreme weather. Finally, a path optimization method based on an improved LSTM network was constructed. Through simulation, it can be seen that compared to the path that does not consider atmospheric effects, the optimized path has significantly improved flightability and safety.

Multi-UAV cluster technology is a hot topic in modern electronic countermeasures research. Based on the “OODA loop” theory of electronic countermeasures, this paper analyzes the effectiveness evaluation method of multi-UAV collaborative electronic countermeasures. We establish an electronic countermeasures correlation state analysis model based on CRITIC weighting and improved gray correlation theory. This method combines multi-UAV collaborative scene examples and use the gray state correlation analysis method based on the dynamic resolution coefficient. We simulate the effects of electronic countermeasures and various influencing factors on the expected task objectives and verify the effectiveness of the proposed method. The results show that this method can better adapt to the dynamic electronic countermeasures scenarios of multi-UAV clusters and has certain participation value for comprehensively and systematically improving the effectiveness evaluation of electronic countermeasures.

A critical challenge degrading the survival probability of penetrators in air penetration missions against networked radar systems stems from inefficient jamming resource allocation, particularly in decision-making quality and real-time responsiveness. To address this challenge, we first construct a many-to-many jamming resource allocation model for jammer formations countering networked radar systems, based on the operational scenario where multiple jammers escort penetrators. The objective function and constraint conditions of this model are rigorously formulated. Subsequently, we propose a Particle Swarm Optimization Guided Seasonal Strategy Nutcracker Optimization Algorithm (PSO-SSNOA) based jamming resource allocation method. This approach innovatively integrates Particle Swarm Optimization (PSO) with the Nutcracker Optimization Algorithm (NOA), while enhancing the nutcracker’s behavioral patterns through seasonal adaptation strategies. Extensive simulation results substantiate PSO-SSNOA’s triple advantage: (1) demonstrating 100% stable convergence probability that surpasses NOA’s 80% while matching PSO’s reliability; (2) achieving 76% faster average runtime than standard PSO implementation; and (3) requiring 90% fewer convergence iterations compared to conventional NOA methodology. These quantified metrics confirm the algorithm’s enhanced stability, real-time responsiveness, and optimization efficacy in complex electronic warfare environments.

Jamming resource allocation is an important step in implementing cooperative jamming. Traditional jamming resource allocation considers only the reduction of enemy radar detection performance but ignores the problem of mutual interference in the formation. Therefore, an adaptive scheduling strategy based on the Improved Sparrow Search Algorithm (ISSA) is proposed to improve the jamming efficiency and minimize mutual interference. First, the target detection model and the mutual interference model are established. On this basis, a two-factor objective function based on network detection probability and the degree of mutual interference is constructed. Then, a whole-process cooperative mechanism is proposed, and an improved circle algorithm and a whale predation strategy are introduced. These strategies are used to overcome the problems of slow convergence and local optimization in the standard sparrow search algorithm (SSA). Finally, the effectiveness and timeliness of ISSA are proved by simulation experiments.