There are massive data and rapidly changing battlefield situations in modern electronic warfare, which is a challenge to jamming resource allocation. It is difficult for the existing optimization algorithms to balance optimization capability and calculation speed at the same time. Above all, this study designs a fuzzy multiattribute evaluation model based on behavioral characteristics. The model comprehensively integrates the characteristics of signal behavior, motion behavior, and external intelligence and can effectively deal with unknown threat signals. Then, this study proposes an improved genetic selection electronic warfare operator (EWO) hyperheuristic (GAEWHH) algorithm. As an emergent optimization algorithm, the HH framework has not previously been applied to the problem of jamming resource allocation. This is a two-level algorithm framework that can isolate problem domains. The high level uses an improved genetic algorithm to search the heuristic space, and four EWOs based on the problem domain are designed for the low level to search the solution space. Combining different EWOs can change the population diversity, evolution direction, and algorithm complexity of the GAEWHH algorithm, which improves the algorithm performance to meet battlefield situation requirements. The experiment shows that for large-scale problems, the GAEWHH algorithm is better than the mainstream evolutionary algorithm in terms of optimization capability and better than Google OR-Tools in terms of calculation speed. In this way, the GAEWHH algorithm achieves a balance between optimization capability and calculation speed.

This paper proposed an improved binary discernibility matrix, and attribute reduction algorithm based on the improved binary discernibility matrix. Sort and simplification links were added in the algorithm, so as to achieve the purpose of reducing complexity of the algorithm. Examples denoted this method reduced the amount of calculation,and it is a practical approach

Dual-aircraft formation slow synchronous blinking jamming is standard for jamming monopulse tracking. However, the jamming signal ratio (JSR) of slow synchronous blinking jamming is low and fluctuant in a dynamic jamming scenario, which leads to a poor jamming effect. This study improves the theory of active cancellation slow synchronous blinking jamming (ACBJ) by combining the cancellation stealth technology with slow synchronous blinking jamming to enhance the slow synchronous blinking jamming. We develop a motion model of dual-aircraft formation in the dynamic jamming scenario. In addition, the dynamic radar cross-section for stealth and nonstealth aircraft is simulated separately. The ACBJ theoretical model and its jamming effectiveness are then derived. We also propose the whole process jamming effectiveness (WPJE) index, and a local-global evaluation index is constructed based on WPJE to evaluate jamming effectiveness reasonably. Finally, we designed four experiments to prove that ACBJ can increase the JSR and improve the jamming effect. The WPJE index for nonstealth aircraft formation using ACBJ was 24% higher than that for slow synchronous blinking jamming, and the WPJE index for stealth aircraft formation using ACBJ increased from 98% to 100%.