## 2 Related Work  
Early satellite communication had low coverage, and research into satellite signal jamming techniques primarily targeted the navigation field [17], which can be divided into three categories: jamming methods, jammer deployment, and jamming efficiency optimization. Jamming methods mainly include blanket jamming and deception jamming. Blanket jamming involves emitting signals with certain bandwidths and higher power on the frequency band where the satellite operates, causing receivers to lose their lock [18; 19]. Studies in the literature [20; 21; 22; 23; 24] have investigated various blanket jamming methods, such as wideband noise jamming, partial-band noise jamming, single-tone jamming, multi-tone jamming, and pulse jamming. Their results demonstrated that blanket jamming has a wide coverage area and can effectively jam target receivers within a certain range. However, blanket jamming also affects communication of other devices and is not suitable for jamming when considering LEO satellite communication. Deception jamming includes repeater deception jamming and generative deception jamming and involves generating deceptive signals that are highly similar to genuine satellite signals or re-transmitting genuine signals, causing receivers to lock onto deceptive signals as genuine ones, resulting in erroneous output. Studies in the literature [25] have carried out jamming with terminals through simulating satellite signals. However, generative deception jamming requires specific knowledge of the genuine signal's format in advance and lacks practical use in LEO satellite communication jamming. Ref. [26] achieved the jamming of an unmanned aerial vehicle (UAV) integrated navigation system through controlling delays and subsequently re-transmitting of satellite signals. Repeater deception jamming does not require extensive prior information but is susceptible to being identified as multi-path jamming by receivers, limiting its effectiveness in LEO satellite communication jamming. The deployment of jammers is an effective means to combat anti-jamming techniques, such as anti-adaptive nulling and beamforming [27]. In [28], a multi-station repeater deception jamming deployment method was proposed based on delay control, which protects fixed targets by analyzing the impact of delays of different magnitudes on the effective jamming region. In [29], a multi-station repeater deception jamming deployment method based on equivalent mapping was presented, which can realize area mapping and trajectory mapping while improving the jamming success rate. In [30], principles for the coordinated deployment of multiple jammers were formulated, and a deployment structure for the coordinated deployment of three jammers was obtained, thereby achieving full-domain coordinated deployment of multiple jammers. However, traditional jammer deployment methods lack flexibility and struggle to adapt to the high-speed movement of LEO satellites. Jamming efficiency optimization can achieve better jamming effects under the same resource configuration conditions [31]. In [32], a particle swarm optimization-based jammer optimization algorithm was proposed, in which the packet delivery ratio was used as the objective function to select the optimal jamming position. However, such single-objective jamming optimization methods cannot counteract the anti-jamming measures of LEO satellites. The study [33] introduced a distributed communication jamming resource allocation method based on improved particle swarm optimization, optimizing the objectives of regional coverage rate and average danger coefficient to address issues such as low efficiency in resource allocation and uncertain jamming benefits in distributed communication jamming. However, such methods only consider two optimization objectives, making it difficult to directly obtain the optimal solution when there are more objective functions.