The assessment of the jamming effect of Synthetic Aperture Radar (SAR) is the primary means to measure the reliability of the jamming, which can provide important guidance for the use of jamming strategies and patterns. This paper proposes a detection-oriented evaluation of the effect of SAR dexterous barrage jamming. Starting from the detection, it divides the evaluation process into two stages: (1) for the case in which the target can be detected under the jamming scenario, two feature parameters, namely, the target exposion area and target relative magnitude, are extracted; (2) for the case in which the target cannot be detected under the jamming scenario, another three feature parameters, namely, jamming relative magnitude, average edge brightness, and local information entropy, are extracted. On this basis, two hierarchical evaluation candidates, the target exposure degree and jamming concealment degree, respectively, are designed, and a comprehensive evaluation index of the dexterous suppression degree is finally proposed. Jamming experiments are carried out from real and simulated SAR data with different scenarios, and the results demonstrate that the proposed method effectively measures the barrage jamming effects of different jamming-to-signal ratios and patterns. More importantly, it quantifies the relationship between suppression degree and detection rate, wherein the detection rate decreases by about 35% to 45% for every 0.1 increase in the suppression degree.

To tackle the challenge of combat resource allocation problem (CRAP), especially under resource constraints, the dilemma between the efficiency of combat resource utilization and the efficiency of problem-solving. We propose a novel genetic algorithm that integrates a restricted tournament selection strategy with sociological principles, named the restricted tournament selection social genetic algorithm (RTS2GA). Initially, we develop a comprehensive model for the allocation of combat resources that takes into account multiple constraints. Then, building upon the traditional Genetic Algorithm, we introduce a novel selection strategy known as restricted tournament selection to enhance the diversity of the algorithm’s population. In addition, we innovatively incorporate the concept of ‘group effect’ from sociology, adding a socialization operator to the algorithm to accelerate convergence and improve the quality of optimal solutions. Comprehensive evaluation confirms RTS2GA’s trade-off profile: though incurring added computational costs, it achieves competitive convergence speed (marginally behind PSO/MPSO; comparable to GA/DE/GA-APSO) while establishing definitive superiority in global optimization across all five benchmarks.