# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 3 JRCO Algorithm  
The relationship between the various modules of the JRCO Algorithm is shown in Figure 2. This section elaborates on the logical relationships between the various modules described in Figure 2 first and the overall process of the algorithm. A detailed analysis and description of the functions of each module are presented later in this section. Unless otherwise specified in this section, let \(i=1,2,\cdots,M,j=1,2,\cdots,N\), \(t=1,2,\cdots,T\). The overall process description of the optimization algorithm was as follows:  
Step 1: Before the JRCO algorithm starts iteration, the system state should be initialized, including the locations of each UAV, the communication coverage radius of each UAV, and the location error variance of each UAV. This step corresponds to Box 1 in Figure 2. Next, the algorithm generates policies for all \(M\) UAVs simultaneously at each time slot \(t\), and the process of generating the policies involves traversing all \(N\) possible values of \(\omega_{i}^{t}\). When the values of \(i\), \(j\), and \(t\) are determined, the current \(i\)-th UAV begins to enter the following sensing and decision-making steps. Step 2: In this step, the \(i\)-th UAV calculates the variance \(\left(\sigma_{\theta,i}^{t}\right)^{2}\) of the AOA measurement error itself based on the communication coverage radius \(\mathbf{r}_{i}^{t}\) selected for the current \(t\)-th time slot. This step corresponds to Box 2 in Figure 2.  
Step 3: The \(i\)-th UAV achieves a target location estimation by networking with its neighboring \(M_{i}\) UAVs. These \(M_{i}\) UAVs broadcast their own AOA measurement results and location information to the \(i\)-th UAV. Specific information before this is introduced later in this section. This step corresponds to Box 3 in Figure 2.  
Step 4: Based on the location, the variance of AOA measurement errors, and the AOA measurement value of these \(M_{i}\) UAVs, the \(i\)-th UAV achieved the estimation of the target location. This estimate could be used to generate the objective function. (This link corresponds to Box 4 in Figure 2). This was because the calculation of the CRLB for target location estimation depended on the location of the target itself, and, in this case, the estimated value of the target location could replace the true value of the target location in the CRLB. Step 5: Due to the location errors of \(M_{i}\) UAVs themselves, in order to calculate the objective function more accurately, the impact of the location errors of each UAV itself was incorporated into the AOA measurement error. The variance of the AOA measurement error at this point was defined as the composite AOA measurement variance. This step corresponds to Box 5 in Figure 2.  
Step 6: Since the communication coverage of each UAV is limited and the UAV needs to achieve optimal deployment through motion, a decision-making mechanism needs to be constructed when the UAV cannot sense any information broadcast by its neighboring UAVs (This state is defined as the current UAV in a disconnected state). When the UAV is not in a disconnected state during a certain time slot, it stores information about itself and  
Figure 2: The flow chart of the JRCO algorithm. the neighboring UAVs in its own memory (The specific types of information are introduced in the second half of this section). When the UAV is in a disconnected state in a certain time slot, the UAV calls the latest networking information in its memory. This link corresponds to Box 6 in Figure 2.  
Step 7: With the guarantee of all the above steps, all UAVs can fully construct an objective function. This link corresponds to Box 7 in Figure 2.  
Step 8: The \(i\)-th UAV generates the current activity based on a random projection gradient descent (RPGD) method under the given \(j\) and \(t\). That is, a policy that adjusts \(\omega_{i}^{t}\) and \(\overline{\mathbf{r}}_{i}^{t}\).