## 2 Principle of Radar Identification Probability Calculation
### Calculation Steps  
In this paper, the actual radar operation process is simplified since we paid more attention to the impact of the limitations of radar resolutions and electromagnetic interference from MGFs, and the relevant assumptions are as follows:  
**Assumption 1**.: _All objects mentioned in this paper are within the theoretical searching range of the radar._  
**Assumption 2**.: _If the line connecting radar and target is occluded by an MGF, the target would be out of radar LOS and cannot be detected or identified._  
**Assumption 3**.: _If there is no LOS limitation or RTA problem, the target would be correctly identified by the radar, regardless of the dynamic environmental factors, such as sea state or weather._  
**Assumption 4**.: _The result probability \(P_{ident}\) only corresponds to the moment when the radar is activated at point \(S\)._Based on these assumptions, the process of calculating the probability \(P_{ident}\) is shown in Figure 3.  
In Figure 3, the specific implementation process of each step is as follows:  
Step 1: Input Data. Based on the 3D model of MGFs, extract the points with pronounced electromagnetic characteristics, denoted as \(B_{i}(X_{Bi},Y_{Bi},Z_{Bi})\) (\(i\) = 1,2, \(\ldots\), \(N\)), and input their planar position and elevation error standard deviation \(\sigma_{BXY}\) and \(\sigma_{BZ}\). According to the aircraft's mission planning and performance parameters, input the radar activation point \(S(X_{S},Y_{S},Z_{S})\) and its planar position and elevation error standard deviation \(\sigma_{SXY}\) and \(\sigma_{SZ}\) (for sea-skimming aircraft, \(Z_{S}\) and \(\sigma_{SZ}\) can be set as 0), aircraft launch platform planar position error standard deviation \(\sigma_{L}\), radar range resolution \(R_{res}\), elevation angle resolution \(E_{res}\), and azimuth angle resolution \(A_{res}\). Finally, input the marine location point \(T(X_{T},Y_{T})\) where \(P_{ident}\) is to be calculated.  
Step 2: Monte Carlo Simulation. Set the Monte Carlo repetition times \(M_{repeat}\) and initialize the counter \(M_{ident}=0\). In each simulation:  
(1) Randomly generate the radar position point \(S^{\prime}(X_{S^{\prime}},Y_{S^{\prime}},Z_{S^{\prime}})\) based on the theoretical values and errors from Step 1 using Equations (5)-(16).  
(2) Determine whether \(T\) is within the LOS of \(S^{\prime}\). If it is, proceed to the next step; otherwise, proceed to the next simulation.  
Figure 3: Flowchart of \(P_{ident}\) calculation for marine areas.  
(3) Sequentially determine whether \(S^{\prime}\), \(T\), and each \(B_{i}\) simultaneously satisfy Equations (1)-(3). If none of them satisfies the condition, increment \(M_{ident}\) by 1; otherwise, proceed to the next simulation.  
Repeat the simulation \(M_{repeat}\) times.  
Step 3: Calculate \(P_{ident}\). Calculate the ratio of \(M_{ident}\) to \(M_{repeat}\) to obtain \(P_{ident}\), as shown in Equation (17):  
\[P_{ident}=\frac{M_{ident}}{M_{repeat}} \tag{17}\]  
Due to the instability of the Monte Carlo method, the accuracy of \(P_{ident}\) can be improved by increasing \(M_{repeat}\) in Step 2.  
By inputting the marine location points around the MGF and repeating the above steps, the distribution of \(P_{ident}\) around the MGF can be visually expressed.