## 3 Scenario Setting and Experimental Metrics  
### MDF-Clustering Simulation Experiments in Complex Environments  
The testing scenarios were designed to closely reflect real-world conditions, ensuring the relevance of the cases for practical applications. For instance, scenarios with missed alarms, trajectory crossover, and cloud clutter were chosen because they represent typical challenges in infrared target tracking environments. The presence of clutter, in particular, creates complex noise conditions that are common in satellite-based tracking systems. By simulating various motion patterns, such as linear, curvilinear, and variable-speed movements, we ensured that the test cases reflect a broad spectrum of potential target behaviors in realistic tracking scenarios. This design allows for an accurate assessment of the proposed algorithm's robustness and reliability under varying conditions, particularly in high-clutter environments. The effectiveness of the MDF-clustering algorithm is evaluated in three distinct simulation scenarios: (1) target leakage, where some alarms fail to detect targets; (2) trajectory crossover, where multiple target trajectories intersect, making differentiation difficult; and (3) cloud clutter, where background noise mimics the target features, complicating the initialization process. In each scenario, the algorithm is tasked with initializing target trajectories based on a combination of numerical and qualitative features. The point trace data collected from these simulations serves as the input for MDF-clustering, which then assigns affiliation degrees to the detected points. The goal is to verify whether the algorithm can successfully initialize the target trajectory even in the presence of significant noise and clutter. The objective is to evaluate the algorithm's ability to successfully initialize target trajectories, particularly in the presence of significant noise and clutter, and to quantify its performance in such challenging environments. In order to verify the robustness of the algorithms, we have compared the methods proposed in this paper with classical methods (NN, Slide window, etc.). All the above algorithms were implemented on a PC (I7-2.8 GHz, I7-8700, 8 GB RAM, MSI, Beijing, China) using Visual Studio 2019. After applying simple filtering, 20 sets of point-trajectory data were randomly chosen for validation in each scenario, resulting in a total of 60 datasets. The marginal distance and symbolic data difference metric were utilized as distance calculation methods within the clustering cluster for the two types of variables. The integrated distance was calculated using Equations (11) and (13) to determine the affiliation results of the point traces. Subsequently, the authenticity of the target was assessed based on the affiliation results. Additionally, statistical weights of different variable types in the three scenarios were computed using Formula (14), and the statistical information was recorded in Tables 1-3, respectively. The figure displays all the point traces and trajectories following the initialization of each algorithm. Figure 1a illustrates a dataset in the simulation scene with trajectory intersection when there are 3 targets, consisting of 60 frames of point trace data and one frame. Figure 1b-f depict the sliding window method, the Hough transform method, the intuition method, the nearest neighbor method, and the initialization results of this paper for this particular set of simulation data in this scene. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable Type** & **Variable** & **Distance Method** & **Weights** \\ \hline \multirow{3}{*}{Number} & velocity & \multirow{3}{*}{Marginal distance} & \multirow{3}{*}{0.734} \\  & spoke brightness & & \\ \cline{1-1}  & contrast & & \\ \cline{1-1}  & coordinates & Euclidean distance & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MDF-clustering assigns average weights under target leakage alarms.