## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Overwhelming Source** & **PCs Subtracted** & **False-Alarm Ratio** & **Detection Ratio** & **Detection Ratio** (**DCBS)** & **Detection Ratio** (**DBSCAN)** \\ \hline
1 & 2 & 0.0039 & 0.90 & 0.23 & 0.00 \\ \hline
2 & 5 & 0.0024 & 0.83 & 0.13 & 0.00 \\ \hline
3 & 5 & 0.0045 & 0.87 & 0.20 & N/A \\ \hline
4 & 5 & 0.0036 & 0.80 & 0.17 & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detection Results on the Four Simulated Scenarios. Figure 14: Target pixel distance from the center of the overwhelming source versus target frequency for the data set with the strongest overwhelming source. This figure shows the undetected targets are due to their proximity to the center of the overwhelming source and/or their lower frequencies. The detected targets are marked with blue dots and the undetected targets with red x’s. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Overwhelming Source** & **False-Alarm Ratio** & **Detection Ratio** \\ \hline
1 & 0.2600 & 1.00 \\ \hline
2 & 0.5563 & 1.00 \\ \hline
3 & N/A & N/A \\ \hline
4 & N/A & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 2: DBSCAN Method Detection Results when using a Binary Decision Mapping. As an additional verification, the PCBS method was applied using both ON and OFF events in the binning and FFT calculation. Table 3 shows that, although the positive detection ratios remain at similar to using only ON events, the false-alarm ratio increases by approximately an order of magnitude when the OFF events were included in the frequency analysis. This increase in false-alarms without an increase in true positive detections reduces the detection confidence of the method when both event types are included. Additionally, four other bin sizes were tested when modifying the data for the FFT. Table 4 provides the results when using a 1, 2.5, 10, and 50 ms bin size for each of the four scenarios. As the table shows, choosing a smart bin size can impact the detection confidence. Using a bin size that is too small introduced high-frequency (>200 Hz) noise in the 1 ms case. Using a bin size that is too large, like in the 50 ms case, reduced noise but removed the detection potential of some of the higher-frequency targets due to the Nyquist frequency limit. The computation time for this method greatly depends on the size of the data (total number of events, recording length, FPA size, etc.) and size of the temporal bins used as the most computationally complex operations are the FFT and solving the eigenvalue problem. This work utilized optimized built in MATLAB functions and ranged from 1 to 3 s (Overwhelming Source 1-4) of computation time on a personal computer containing an 11 th Generation Intel Core i7-11800H at 2.50 GHz, 16 logical processors, and 64 GB of RAM. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**1 ms Bin Size**} & \multicolumn{2}{c}{**2.5 ms Bin Size**} \\ \hline
**Overwhelming** & **False-Alarm** & **Detection** & **False-Alarm** & **Detection** \\
**Source** & **Ratio** & **Ratio** & **Ratio** \\ \hline
1 & 0.0020 & 0.97 & 0.0015 & 0.90 \\ \hline
2 & 0.0277 & 0.90 & 0.0060 & 0.87 \\ \hline
3 & 0.0057 & 0.93 & 0.0027 & 0.87 \\ \hline
4 & 0.1483 & 0.87 & 0.0060 & 0.80 \\ \hline \multicolumn{5}{c}{**10 ms Bin Size**} & \multicolumn{2}{c}{**50 ms Bin Size**} \\ \hline
**Overwhelming** & **False-Alarm** & **Detection** & **False-Alarm** & **Detection** \\
**Source** & **Ratio** & **Ratio** & **Ratio** & **Ratio** \\ \hline
1 & 0.0030 & 0.90 & 0.0006 & 0.73 \\ \hline
2 & 0.0012 & 0.87 & 0.0000 & 0.63 \\ \hline
3 & 0.0030 & 0.87 & 0.0000 & 0.60 \\ \hline
4 & 0.0054 & 0.83 & 0.0029 & 0.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PCBS Method Detection Results when using Different Bin Sizes.