## 3 Experiments and Analysis  
### Datasets  
To validate the practical effectiveness of STFD, this study utilized two datasets from the National University of Defense Technology [42; 43; 44]. Six image sequences were selected, each consisting of 100 frames, covering various complex scenarios, including cluttered backgrounds, dim targets, and varying target speeds. In Figure 6 and Table 1, we present sample frames from these six datasets, along with brief descriptions of their content. To validate the heterogeneity among these datasets, we conducted Kolmogorov-Smirnov(KS) tests between different dataset pairs. The results shown in Table 2 demonstrate significant differences in data distributions across the datasets, which statistically justifies the rationality of our dataset selection.  
Among them, Seq.1 to Seq.3 have a size of \(256\times 256\), with relatively complex backgrounds and slow-moving targets. Seq.4 to Seq.6 have a size of \(512\times 640\), where Seq.4 and Seq.5 feature relatively clean sky backgrounds, and Seq.6 includes a background comprising both sky and urban areas, with targets of varying speeds moving across these two regions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sequence** & **Frame** & **Image Size** & **Target Size** & **Target** & **Background Description** & **SCR** \\ \hline Seq.1 & 100 & \(256\times 256\) & \(3\times 3\) & \(1\) & Complex rural ground & 7.02 \\ \hline Seq.2 & 100 & \(256\times 256\) & \(2\times 2\)-\(3\times 3\) & \(1\) & \begin{tabular}{c} Complex forest terrain with \\ terrain-induced glare \\ interference \\ \end{tabular} & 5.81 \\ \hline Seq.3 & 100 & \(256\times 256\) & \(1\times 1\)-\(3\times 3\) & \(1\) & \begin{tabular}{c} Complex hilly terrain \\ background with \\ overlapping clutter \\ interference caused by \\ terrain reflections \\ \end{tabular} & 2.94 \\ \hline Seq.4 & 100 & \(256\times 256\) & \(3\times 3\) & \(2\) & \begin{tabular}{c} Multi-target scenario with \\ cluttered cloud background \\ \end{tabular} & 10.98 \\ \hline Seq.5 & 100 & \(256\times 256\) & \(1\times 1\)-\(3\times 3\) & \(2\) & \
\begin{tabular}{c} Relatively clear sky \\ background \\ \end{tabular} & 15.88 \\ \hline Seq.6 & 100 & \(256\times 256\) & \(1\times 1\)-\(3\times 3\) & \(1\) &
\begin{tabular}{c} Urban background under \\ haze conditions \\ \end{tabular} & 7.32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sequence** & \multicolumn{5}{c}{**Comparison Sequence**} \\ \cline{2-7}  & **Seq.1** & **Seq.2** & **Seq.3** & **Seq.4** & **Seq.5** & **Seq.6** \\ \hline Seq.1 & – & 0.148/0.000 & 0.238/0.000 & 0.620/0.000 & 0.711/0.000 & 0.442/0.000 \\ Seq.2 & 0.148/0.000 & – & 0.198/0.000 & 0.568/0.000 & 0.669/0.000 & 0.363/0.000 \\ Seq.3 & 0.238/0.000 & 0.198/0.000 & – & 0.403/0.000 & 0.480/0.000 & 0.245/0.000 \\ Seq.4 & 0.620/0.000 & 0.568/0.000 & 0.403/0.000 & – & 0.152/0.000 & 0.447/0.000 \\ Seq.5 & 0.711/0.000 & 0.669/0.000 & 0.480/0.000 & 0.152/0.000 & – & 0.478/0.000 \\ Seq.6 & 0.442/0.000 & 0.363/0.000 & 0.245/0.000 & 0.447/0.000 & 0.478/0.000 & – \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kolmogorov–Smirnov test between raw experimental sequences (D-statistic/\(p\)-value).  
Figure 6: Visual and 3D surface analysis across sequences. (**a1–f1**) Infrared images from Seq.1 to Seq.6, with targets highlighted in red boxes and magnified in the image corners. (**a2–f2**) Corresponding 3D surface mesh diagrams for each sequence. Targets in the figure are annotated with red bounding boxes and magnified.