# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 1 Introduction
Before the dual-functional radar-communication (DFRC) was well studied, most of the works related to UAV-assisted networks were discussed from the perspective of communication and radar separately [21; 22; 23; 24]. However, with the recent development of modern communication technology, combining communication and radar sensing, which is known as DFRC, has attracted extensive attention in the research community [25; 26; 27]. Based on technology, the communication and radar module could efficiently share the same hardware and obtain the integration gain. In [28], the authors analyzed the advantages of DFRC in self-driving cars to achieve substantial gains in cost, performance, and robustness. The authors of [29] employed the reconfigurable intelligent surface in the DFRC system to improve both the performance of sensing and communication. The sparse antenna array configurations by antenna selection and waveform attend pairing to achieve a higher data rate are investigated in [30]. In addition, the authors of [31] present a hardware prototype of the DFRC system, which utilizes generalized spatial modulation to improve communication performance and reduce the sidelobe level of the transmit beam pattern. Although the development of DFRC has brought new vitality to the applications of UAVs, the increased complexity induced by the integrated system may lead to more challenges to the optimization design. The traditional method usually optimized the communication performance metrics under the radar constraints or vice versa [32]. Nevertheless, the lack of constraints in one of the specific problems will inevitably affect the final optimization result and the performance trade-off between the two functionalities. Evolutionary multi-objective optimization is beneficial in dealing with such issues. Since the 1990s, the algorithms used to solve the problem with two or three objectives have been proposed [33; 34; 35] and wildly used in various wild areas such as smelting production measurement systems [36], game map generation [37], and water distribution systems [38]. Some of the typical methods such as the \(\epsilon\) dominance and grid-dominance are driven primarily by concerns about the development of new dominance relations. In contrast, another class of methods such as the non-dominated sorting genetic algorithm II (NSGA-II) focuses on improving diversity to rescue the loss of selection pressure [39; 40]. Moreover, there are some methods which find a preferred subset of solutions to shrink the search space and employ dimension reduction to classify the embedded efficient front in the objective neighborhood space. Among the existing methods, the many-objective optimization algorithm based on dominance and decomposition (MOEA/DD), which integrates the dominance and decomposition approaches, effectively solves the conflicts between convergence, diversity, and computational efficiency [41]. This method proposes a systematic framework to develop a widely spread weight vector in which each vector specifies a unique sub-region in the objective space. Then, it releases the pressure of non-domination and computational efficiency when the number of objectives grows. The constraint consideration is also included in the process of the algorithm, especially when the problem involves a massive number of objectives. Therefore, the MOEA/DD method gives a bright path to solving the multi-objective problem in the DFRC system to optimize the performance of radar and communication simultaneously. Moreover, under the limited resources of the marine scenario, balancing the advantages of UAVs and DFRC can effectively improve the system's performance and promote spectral efficiency. However, building a UAV-assisted DFRC data acquisition system to achieve both higher data transmission throughput and radar localization accuracy needs to consider some critical issues, such as the buoy transmit power and the UAV's location. On the one hand, obtaining the optimal location of the UAV can effectively improve the throughput of the system.