# On-Board Parameter Optimization for Space-Based Infrared Air Vehicle Detection Based on ADS-B Data
## 1 Introduction
The method accounted for the aircraft's geometry, material properties, engine exhaust, flight conditions and detector position. [PERSON] et al. [11; 12] established an all-attitude motion model for mid-wave and long-wave infrared space-based detection scenarios; the optimal detection spectrum can be selected through the signal-to-interference ratio. [PERSON] et al., [PERSON] et al. and [PERSON] et al. [13; 14; 15; 16] established an infrared radiation calculation model of the aircraft exhaust system and body according to the infrared radiation characteristics of aircraft. Considering the atmospheric attenuation along the radiation transmission path and the background noise received by the detector, the infrared characteristics of aircraft under different working conditions were analyzed. [PERSON] et al. [17] studied the detection spectrum optimization problem of stealth aircraft targets on space platforms. By establishing a radiation model of stealth aircraft targets and backgrounds, the impact of different infrared bands on the detection performance of stealth aircraft targets was analyzed and the optimal detection band range was given. [PERSON] et al. [18] proposed a method based on artificial neural networks to identify the most useful spectral range for target discrimination. [PERSON] et al. [19] proposed a target-constrained interference-minimized band selection method to improve the performance of target detection in hyperspectral images. The method uses the spectral information of targets and backgrounds, as well as the characteristics of target detectors, to select the optimal subset of spectral bands that maximizes the signal-to-noise ratio of target detection while minimizing interference. [PERSON] et al. [20; 21] combined cloud top temperature, cloud pattern, cloud top height and other meteorological satellite sensor data to generate three-dimensional atmospheric transmittance and atmospheric path thermal radiation texture under different cloud heights and cloud phases. The results showed that the detection ability in the narrow bands of 2.65-2.90 \(\upmu\)m and 4.25-4.50 \(\upmu\)m was better than that in the broadband band of 3-5 \(\upmu\)m. In the aspect of aerial target detection and tracking, infrared image sequences from different platforms are important input sources for downstream target detection and tracking algorithms, and image quality directly determines the final detection and tracking performance. Several studies have carried out the evaluation of an enhanced YOLO target tracking algorithm based on infrared images obtained by a UAV platform [22; 23; 24; 25; 26; 27]. Most of the previous studies focus on the infrared characteristic band selection to increase the contrast of the target and background, thereby improving the detectability of the system. To the best of our knowledge, existing studies have established spectral band selection methods based on infrared detection models and background clutter, but the detection scene changes of the daytime and nighttime differences are less considered, and the influence of different cloud backgrounds is not completely justified. The flight mode changes of air targets, such as reduced thrust takeoff or changes in cruising altitude and speed, have not been fully considered. This paper proposes an on-board parameter optimization method based on prior ADS-B information to solve the above problems. The infrared signatures of multi-phase air targets and marine cloud backgrounds are analyzed. A space-based AVD model is established and the system performance is evaluated. The numerical simulation results show that the proposed method can effectively improve the detection robustness in different flight phases and backgrounds compared with the traditional fixed-parameter system. Our method can be generally applied to most space-based infrared systems for in-orbit performance improvement; meanwhile, it provides theoretical support for a new generation detection system of in-orbit adaptive parameter adjustment.