# On-Board Parameter Optimization for Space-Based Infrared Air Vehicle Detection Based on ADS-B Data
## 3 Results and Discussion
### On-Board Spectral Band Optimization
Under night detection conditions (e)-(f), 2.45-2.95 \(\upmu\)m was the preferred band, but the \(JSNCR\) was lower and only greater than three. The overall optimization results are summarized in Table 2. It can be found that bringing in real-time flight data is closer to the actual detection scenario. \(JSNCR\) improved significantly during the daytime after the spectral band optimization; at the same time, it was also found that low \(JSNCR\) was common in the AVD during the nighttime. For the cruising phase of the flight route in the previous section, its dynamic speed curve is shown in Figure 10. During the cruising period from 1700 s to 4100 s, the flight speed monotonously increased from 0.47 Ma to 0.62 Ma. Based on a \(GSD\) of 70 m and the limit of air target flying no more than one pixel, the maximum integration time was 433 ms to 331 ms, which matched the velocity curve, as shown on the right axis in the figure. According to the result of spectral band optimization, the detection spectral band was preferably 2.8-3.2 \(\upmu\)m during the daytime for the cloud backgrounds, whose radiance was from 0.088 to 0.03 W/Sr/m\({}^{2}\). As the current band selection was a narrow band, and the target radiation intensity was only in several hundreds of W/Sr, there was no need to consider the saturation due to the strong target, and the maximum integration time was mainly limited by the target flight speed The comparison between the fixed-parameter system spectrum of 2.7-3.0 \(\upmu\)m and the optimized \(JSNCR\) performance improvement is shown in Figure 11a and Table 3. It can be seen that the overall performance was improved after adjusting the detection spectrum, especially for the cirrus cloud background. \(JSNCR\) increased about four times, greatly improving the detectability under strong background clutter during the daytime. Furthermore, based on the spectral band optimization, we performed a dynamic parameter adjustment of the maximum integration time and the matching of the integration capacitor gear, and the result is shown in Figure 11b and Table 3. It can be seen from the results that the parameter optimization can only improve the detection scenario dominated by non-background clutter, and had no obvious improvement on the performance of the cirrus cloud background, while for the cumulus and altostratus background with weaker background clutter, there was still a certain effect. The corresponding \(JSNCR\) increased by 1.16 to 1.31 times, and \(NET\) reduced from 2.4 W/Sr to 1.2 W/Sr. Figure 9: \(Cont.\)  
Figure 9: The spectral band optimization results of \(JSNCR\) with different backgrounds. (**a**) Daytime cumulus cloud; (**b**) daytime altostratus cloud; (**c**) daytime cirrus cloud; (**d**) daytime sea; (**e**) nighttime cumulus cloud; (**f**) nighttime altostratus cloud; (**g**) nighttime cirrus cloud; (**h**) nighttime sea. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{\(JSNCR\)**\#Spectral Band (\(\mu\)m)**} \\ \hline \multicolumn{2}{c}{**Background**} & \multicolumn{2}{c}{**Fixed Parameter**} & \multicolumn{2}{c}{**Proposed**} \\ \hline \multirow{4}{*}{daytime} & Cumulus cloud & [EMAIL_ADDRESS]–3.0 & [EMAIL_ADDRESS]–3.244 \\  & Altostratus cloud & [EMAIL_ADDRESS]–3.0 & [EMAIL_ADDRESS]–3.287 \\  & Cirrus cloud & [EMAIL_ADDRESS]–3.0 & [EMAIL_ADDRESS]–3.112 \\  & Sea & [EMAIL_ADDRESS]–3.0 & [EMAIL_ADDRESS]–2.95 \\ \hline \multirow{4}{*}{nighttime} & Cumulus cloud & [EMAIL_ADDRESS]–3.0 & [EMAIL_ADDRESS]–2.95 \\  & Altostratus cloud & [EMAIL_ADDRESS]–3.0 & [EMAIL_ADDRESS]–2.95 \\ \cline{1-1}  & Cirrus cloud & [EMAIL_ADDRESS]–3.0 & [EMAIL_ADDRESS]–2.95 \\ \cline{1-1}  & Sea & [EMAIL_ADDRESS]–3.0 & [EMAIL_ADDRESS]–2.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The optimization results of spectral band and \(JSNCR\).