# On-Board Parameter Optimization for Space-Based Infrared Air Vehicle Detection Based on ADS-B Data
## 2 Methodology
### On-Board Parameter Optimization  
#### 2.3.1 On-Board Spectral Band Optimization  
The proposed method based on public ADS-B information can adjust the detection parameters more accurately and maximize the detection efficiency. Compared with the conventional radiation model calculation based on fixed flight height and speed, it is more accurate. During the flight, the spectral band is traversed to calculate the optimal band under different backgrounds. According to the characteristics of the target and the background, \(JSNCR\), which combines \(SNR\) and \(SCR\), is carried out in this paper as the criteria to perform spectrum traversal optimization. It is expressed as  
\[\left\{\begin{array}{c}SCR(\lambda,\Delta\lambda)=\frac{K\cdot EE\cdot J( \lambda,\Delta\lambda)}{CET(\lambda,\Delta\lambda)}=\frac{K\cdot N_{\text{g}}( \lambda,\Delta\lambda)}{\sqrt{[\sigma(\lambda,\Delta\lambda)N_{\text{bg}}^{2} }}\\ \\ SNR(\lambda,\Delta\lambda)=\frac{K\cdot N_{\text{g}}(\lambda_{\text{log}}, \Delta\lambda)}{\sqrt{N_{\text{g}}(\lambda,\Delta\lambda)+N_{\text{bg}}( \lambda,\Delta\lambda)+N_{\text{instr}}(\lambda,\Delta\lambda)+N_{\text{dark}} +N_{\text{read}}^{2}}}\\ \\ JSNCR\cdot(\lambda,\Delta\lambda)=\frac{K\cdot N_{\text{g}}(\lambda,\Delta \lambda)}{\sqrt{N_{\text{s}}(\lambda,\Delta\lambda)+N_{\text{bg}}(\lambda, \Delta\lambda)+N_{\text{instr}}(\lambda,\Delta\lambda)+N_{\text{dark}}+N_{ \text{read}}^{2}+[\sigma(\lambda,\Delta\lambda)N_{\text{bg}}^{2}}}\end{array} \right., \tag{10}\]  
where \(\lambda\) is the lower band edge of the spectral band and \(\Delta\lambda\) is the bandwidth.  
The threshold of different criteria may vary according to the actual detection scenario: \(SCR\) threshold needs to be considered in strong clutter and weak clutter. Generally, \(SCR\) > 10 can be considered as an ideal weak clutter. When \(SCR\) is less than 1, it is considered to be a strong clutter scene. In this case, the clutter dominated the final detection performance [34; 35]. Since there was no background clutter greatly affecting detection at nighttime, the analysis focused on the optimization of a given background band that was easily affected by strong stray radiation during the daytime. In this paper, we set the clutter ratio \(\sigma\) as 5% during the daytime.  
The \(SNR\) threshold was generally set to 6 for target detection and 3 for target tracking. The target recognition was better than 30 [36]. \(JSNCR\) reflects the influence of both \(SNR\) and \(SCR\) at the same time; the \(JSNCR\) optimization problem in the cruising phase is similar to that of the target tracking process. Considering the weak infrared signature of air targets at nighttime and the current status of multi-frame correlation for moving target detection, the final \(JSNCR\) threshold was set to greater than 3 [37].  
The on-board parameter optimization flow during the detection can be divided into following four main steps.  
First of all, build the detection scenario according to a typical civil air target flying above a marine background with clouds; initialize the main parameters such as the detection band, integration time and integral capacitance of the system; and calculate the current \(SCR\), \(SNR\) and \(JSNCR\).  
Secondly, obtain the real-time flight state data from ADS-B, including flight altitude and speed, combined with the background spectral characteristics around the target area.  
Then, set (\(\lambda\), \(\Delta\lambda\)) as variables, where \(\lambda\) starts from 2.0 to 4.0 \(\upmu\)m and \(\Delta\lambda\) is from 0.1 to 0.5 \(\upmu\)m. Calculate the \(JSNCR\) under a specific background, and the (\(\lambda\), \(\Delta\lambda\)) corresponding to the obtained maximum \(JSNCR\) value is the result after spectrum optimization.  
Finally, adjust the most reasonable detection band for the current scenario.