###### Abstract  
UAVs can be deployed in many scenarios to provide various types of services via 6G edge communication. In these scenarios, it is necessary to obtain the position of the UAVs in a timely and accurate manner to avoid UAV collisions. In this paper, we consider improved passive localization algorithms aimed at reducing convergence time and adapting to extreme conditions. For the sake of reducing the complexity of signals and ensuring the reliability of receiving processes, we reconsidered the angle between arrival signals as the feature in positioning. Then, according to the characteristics of the positioning process, we draw on the cyclical process of the iterative greedy algorithm to construct the coding, destruction, and reorganization process to guide the movement of the UAV. Moreover, an improved Metropolis criterion is added to prevent falling into the local optimal solution. Finally, the proposed algorithm is verified in the simulation results. The results show that the algorithm can achieve precise positioning and excellent track planning within a small number of iterations, and it reduces the amount of information carried by the signal and convergence time compared with the traditional method. autonomous control system; UAV; passive positioning; edge communication; 6G +
Footnote †: journal: Medical  
transmit signals outward, and then other UAVs can passively receive signals to realize their own positioning, which only needs to be based on communication characteristics. From the perspective of users, this way of positioning can effectively protect and hide UAV swarms [6; 7; 8]. Recently, passive positioning has shown great potential. [PERSON] et al. [9] fused TDOA (time difference of arrival) with DOA (difference of arrival) to achieve passive positioning and discussed the effects of different delays and spatial structures on positioning, [PERSON] et al. [10] used a single-target optimization model to guide the movement of UAV in passive positioning. In the process of UAV flight, [PERSON] et al. [11] used [PERSON]'s iterative algorithm to establish multiple nonlinear equations and then completed the positioning of the formation. [PERSON] et al. [12] introduced the concept of BR (bistatic range) matrices, which effectively removed noise in passive positioning. Although passive positioning is less restrictive and more concealed, we can still further improve the method to enhance its advantages. Specifically, (1) most of the literature studies the improvement of mainstream algorithms, such as TDOA and other algorithms that use signal time difference of arrival. However, it is also of great significance to study the use of other characteristics of the signal to construct algorithms. (2) Passive positioning is mostly used in electronic countermeasures for BS-aircraft (base station) or aircraft-aircraft positioning, but it can also be used for internal positioning of UAV swarms in closed environments [13; 14; 15]. (3) Although there is a consensus to improve positioning using GPS by considering different environments, there is little research on positioning that does not rely on GPS when considering different environments. Motivated by the above issues, we explore a passive localization system using a small number of geometric factors combined with environmental noise, and a UAV autonomous control system is constructed based on this. In detail, geometric factors contain range and bearing measurements, such as azimuth and TDOA [16; 17], among which we select the angle between two arriving signals only to implement the algorithm. In addition, we use for reference the iterated greedy algorithm [18] and give the specific movement strategy of the UAV swarm. The main contributions of this paper are summarized as follows. * We investigate the application of passive localization in the context of a new user localizing itself rather than the transmitting source by being dynamically received, and we then formulate a localization algorithm based on a hyperbolic position system.