## 2 Related Work  
Research related to LEO navigation signal jamming is divided into four main categories: jamming methods, jammer deployment, jamming effectiveness assessment, and jamming station optimisation. Jamming methods for LEO satellites are mainly categorised into blanket jamming and deception jamming [22]. Blanket jamming covers a wide range of areas, has a high success rate of jamming in the absence of countermeasures, and is able to further improve the performance of suppression jamming in terms of the form of the signal and the antenna from which the signal is transmitted [23; 24]. In [25], constant modulus interference waveforms are efficiently generated with the help of CD algorithms in standard PREW scenarios, and precise blanket jamming is performed on specific targets by energy focusing. However, blanket jamming is less stealthy and easily interferes with our equipment. Deception jamming is mainly classified into repeater deception jamming and generative deception jamming. Generative jamming directly generates spoofed signals similar to LEO signals, intrudes into enemy receivers, and directly changes their position information [26]. In [27], generative adversarial networks (CTGANs), Gaussian copulas, and variational autoencoders (VAEs) are utilised to generate synthetic jamming attacks from a widely used UAVs attack dataset. In [28], a novel traction strategy using traction codes is proposed to suppress some of the authentication signals and form the desired correlation peaks. Generative jamming is covert and efficient, but generative jamming needs to be based on the premise that the form of LEO signals is known, and the form of military LEO signals is strictly encrypted, which makes it difficult to obtain all the information from these signals. Repeater deception jamming works by receiving and processing the real signal and then forwarding it out with a delay, without needing to know all the information about the signal [29]. In [30], repeater deception jamming techniques for creating false locations, false times, and false speeds are theoretically demonstrated. Jammer deployment is an effective means of countering anti-jamming techniques, such as adaptive zeroing and beamforming. In [31], a joint optimisation scheme for Aerial Reconfigurable Intelligent Surfaces (ARISs) deployment and passive beamforming is developed for the deployment of a single jammer. In [32], an innovative distributed jammer centre-of-mass localisation method based on clustered centre-of-mass convergence is proposed to achieve an accurate estimation of the number of distributed jammers. However, it is difficult to implement effective jamming of highly dynamic, narrow-beam LEO signals with conventional single- or multi-site jammer deployments. Establishing a jamming effectiveness evaluation criterion is a prerequisite for performing jamming optimisation. A reasonable evaluation criterion enables the optimisation to converge quickly and achieve efficient and fast interference. In [33], based on the existingresults of the evaluation of the effectiveness of navigation interference, an index system for the evaluation of the effectiveness of satellite navigation receivers is constructed from four levels. In [34], a new method based on Convolutional Neural Networks (CNNs) for spoofing interference evaluation is proposed. In [35], a distributed jammer full-area cooperative deployment method is proposed, and the evaluation of the deployment effects, such as maximum spoofing range, spoofing success rate, pseudo-range rate, and power control, are carried out. Optimisation for distributed jammer deployment not only improves jamming effectiveness but also reduces unnecessary losses. In [36], the problem of targeted allocation of weapon targets for air defence operations was solved using dynamic programming. In [37], the nonlinear combinatorial optimisation problem for the dynamic combination of weapon resources and objective matching on different platforms is solved using the 0-1 integer programming method.