# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 3 Method
### Parameter Initialization  
The initial UAV's location and buoy transmit power are obtained following the process in Algorithm 2. The initial process consists of four parts, i.e., the initialization of parent population \(I_{\mathbf{U}}\) and \(\mathbf{p}\), generation of the weight vectors, the non-domination level processing, and the neighborhood generation, which initialize the parameters utilized for the upcoming stage. To be detailed, the initial values of \(I_{\mathbf{U}}\) and \(\mathbf{p}\) are randomly sampled within the corresponding range uniformly. Then, [PERSON] and [PERSON]'s approach is employed to generate the weight vectors \(\mathbf{W}=\{\mathbf{w}_{1},\ldots,\mathbf{w}_{N}\}\)[53]. In this step, \(N\) points of weight vectors are sampled with a uniform spacing \(\delta=1/U\), where \(U\) is the number of considerations along each target objective function. It should be pointed out that the choice of \(U\) has to be greater than the number of object functions that guarantee our population diversity.  
The sub-region in the objective space \(\Pi^{i}\) specified by each weight vector \(\mathbf{w}_{i}\) can be defined as  
\[\Pi^{i}=\{(-F_{1},F_{2})^{T}\in\mathbb{R}^{2}\}|\ \langle(-F_{1},F_{2})^{T},\mathbf{w}_{ i}\rangle\leq\langle(-F_{1},F_{2})^{T},\mathbf{w}_{j}\rangle\}, \tag{15}\]  
where \(j\in\{1,2,\ldots,N\}\), \(\langle(-F_{1},F_{2})^{T},\mathbf{w}_{j}\rangle\) is the acute angle between \(\langle(-F_{1},F_{2})\rangle^{T}\) and \(\mathbf{w}_{j}\). The neighborhood of each weight vector \(\mathbf{w}_{j}\) consists of \(M\) closest weight vectors which are calculated by Euclidean distances. Subsequently, the parameters are divided into several non-domination levels by the non-dominated sorting method, and each value in \(\mathbf{l_{U}}\) and \(\mathbf{p}\) is randomly associated with a unique sub-region.
```
1:Obtain the sub-region associated with offspring solution \(\mathbf{l^{off}_{U}}\) and \(\mathbf{p^{off}}\) based on Equation (15).
2:\(\mathbf{l^{up}_{U}}\leftarrow\mathbf{l^{(i)}_{U}}\cup\{\mathbf{l^{off}_{U}}\}\); \(\mathbf{p^{hp}}\leftarrow\mathbf{p}^{(t)}\cup\{\mathbf{p^{off}}\}\).
3:\(\mathbf{l^{is}_{U}},\mathbf{p^{is}}\leftarrow\emptyset\).
4:for\(\mathbf{l_{U}}\in\mathbf{l^{hp}_{U}};\mathbf{p}\in\mathbf{p^{hp}}\)do
5:if\(CV(\mathbf{l_{U}},\mathbf{p})>0\)then
6:\(\mathbf{l^{is}_{U}},\mathbf{p^{is}}\leftarrow\{\mathbf{l^{is}_{U}},\mathbf{p^{is}}\}\cup\{\mathbf{l _{U}},\mathbf{p}\}\).
7:endif
8:endfor
9:Case I:\(\left|\{\mathbf{l^{is}_{U}},\mathbf{p^{is}}\}\right|=0\)
10: Update parent population of parameters according to Algorithm 5.
11:Case II:\(\left|\{\mathbf{l^{is}_{U}},\mathbf{p^{is}}\}\right|\
eq 0\)
12: Sort \(\{\mathbf{l^{is}_{U}},\mathbf{p^{is}}\}\) in descending order regard to (16).
13:\(sign=0\).
14:while i=1 to \(\left|\{\mathbf{l^{is}_{U}},\mathbf{p^{is}}\}\right|\)do
15:if\(\{\mathbf{l^{is}_{U}}(i),\mathbf{p^{is}}(i)\}\) sub-region is not isolated then
16:\(sign=1\), \(\mathbf{l^{i}_{U}}\leftarrow\mathbf{l^{is}_{U}}(i),\mathbf{p^{\prime}}\leftarrow\mathbf{p^{is }}(i)\).
17: break.
18:endif
19:endwhile
20:if\(sign=0\)then\(\mathbf{l^{\prime}_{U}}\leftarrow\mathbf{l^{is}_{U}}(1),\mathbf{p^{\prime}}\leftarrow\mathbf{p^{is }}(1)\).
21:endif
22:\(\mathbf{l^{(t+1)}_{U}}\leftarrow\mathbf{l^{hp}_{U}}(\mathbf{l^{\prime}_{U}},\mathbf{p^{(t+1)}} \leftarrow\mathbf{p^{hp}}\backslash\mathbf{p^{\prime}}\).
```
**Algorithm 4** Constraint Operation