# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 2 System Model and Problem Formulation  
### System Model  
As illustrated in Figure 1, we propose a maritime monitoring data collection system, where a rotary-wing UAV is deployed as a movable access point (AP) to collect data from \(K\) single-antenna buoys based on the NOMA principle. Meanwhile, the buoys cooperatively perform radar sensing to localize and track a target through the DFRC signals. The scenario we considered in this paper involves a single data collection process by the UAV and can easily be extended to cover multiple collection processes. All of the buoys are randomly distributed on the sea, and the set of the buoy is denoted as \(\mathcal{K}=\{1,2,\ldots,K\}\). Without loss of generality, a three-dimensional (3D) Cartesian coordinate system is considered in this paper. To be detailed, the horizontal coordinates of the communication UAV and each buoy are denoted as \(I_{\mathbf{U}}=[x,y]^{T}\in\mathbb{R}^{2\times 1}\) and \(I_{k}=[l_{x}(k),l_{y}(k)]^{T}\in\mathbb{R}^{2\times 1}\), respectively. Although sea clutter, sidelobe, and subspace interference are crucial in marine environments, to present our proposed DFRC system more clearly and simplify the computational steps, we assume that the buoy is relatively stationary at this point [42; 43]. Following the assumption in [32], we only consider the UAV's hovering location and ignore the process of the UAV takeoff and landing. And the UAV is assumed to fly at an unchangeable altitude \(H_{U}\). Note that the locations of buoys and the UAV are assumed to be almost stable during a short period; thus, the channels remain almost constant, which makes it possible to use the pilot signals to obtain channel state information (CSI). Note that the buoys' power source is not considered in this system and the transmit power can be optimized when the channel information is known according to the recent development of the energy deployment strategy [44]. In this paper, we assume that all of the UAV-buoy links are referred to the LoS channel model. And the Doppler effect can be perfectly compensated because of the UAV's mobility [45]. In order to specify the characteristics of the UAV-assisted DFRC ocean monitoring system, the coordinates of all buoys and the CSI of all data links are assumed to be perfectly known by the UAV [46]. Let \(h_{Uk}\) denote the channel gain between the UAV and the \(k\)-th buoy. Then, for \(k\in\mathcal{K}\), we have  
\[h_{Uk}=g_{0}d_{Uk}^{-2},\ \forall k\in\mathcal{K}, \tag{1}\]  
where \(g_{0}\) denotes the channel gain at the reference distance \(d_{r}=1\) m, \(d_{Uk}=\sqrt{||I_{\mathbf{U}}-I_{k}||^{2}+H_{U}^{2}}\) denotes the distance between the UAV and the \(k\)-th buoy. As mentioned above, each buoy transmits its signal non-orthogonally to the UAV on the same radio spectrum with controlled transmit power. And the SIC is employed at the UAV to guarantee the experience of distinct channel gains. Toward this end, by assuming that the channel gain of each buoy decreases sequentially, i.e., \(h_{U1}>h_{U2}>\ldots>h_{Uk},k\in\mathcal{K}\), we propose to dynamically adjust the transmit power in each buoy. Specifically, through  
Figure 1: UAV-assisted DFRC data collection in ocean monitoring system. SIC, the signals from stronger data links will be successively decoded while the others with lower channel gains are treated as noise. Therefore, the throughput of each buoy depends on the signal interference from the buoys with lower channel gains. Consequently, the throughput for the \(k\)-th buoy can be written as  
\[R_{k}=B\text{log}_{2}\left(1+\frac{p_{k}h_{Uk}}{\sum_{i=k+1}^{K}p_{i}h_{Uli}+ \sigma^{2}}\right),\ \forall k\in\mathcal{K}, \tag{2}\]  
where \(p_{k}\) is the transmit power of the \(k\)-th buoy, \(\sigma^{2}\) is the additive white Gaussian noise (AWGN) power and \(B\) is the transmission bandwidth.