## 5 Experimental Results and Comparison  
### Experimental Results  
The hardware architecture needs to be calculated in the fixed-point format. In contrast, the accuracy of the software simulation is double-precision floating point. Therefore, in the process of hardware implementation, due to the existence of the truncation error, the implementation result of the algorithm will be biased, which can be solved by choosing the appropriate digit width of the fixed-point number. To address this, the precision loss data under different fixed-point bits are analyzed first. Based on these considerations, the relative error calculation method is designed in this paper:  
\[error=\frac{\sum|Result_{software}(i,j)-Result_{hardware}(i,j)|}{\sum|Result _{software}(i,j)|},(Result_{software}(i,j)>16) \tag{42}\]  
Since the background of this paper pays more attention to the highlighted points (the higher gray value), the elements with a gray value of 16 and below are not considered here.  
The results are shown in Figure 10. It can be seen that with the increase in fixed, the relative error shows a decreasing trend. After 11 digits, there is no obvious change, and the relative error stabilizes at 0.87%.  
Figure 10: Relative error with the increase in fixed. Fixed refers to the number of decimal places in a fixed-point number.  
To further validate the robustness and practical performance of our proposed method, this study employs a comprehensive and diverse infrared image dataset [24]. This dataset is specifically designed for the detection and tracking of small low-altitude flying targets and is generated through field recordings and post-processing, making it particularly suitable for the detection of fixed-wing unmanned aerial vehicles in complex backgrounds. The data collection encompasses a variety of environmental conditions, including sky and cluttered backgrounds, to simulate the diverse challenges encountered in real-world scenarios. The entire dataset consists of 22 image sequences, 30 trajectories, 16,177 frames, and 16,944 annotated target instances. Each target is associated with a clearly defined position label, and each image sequence corresponds to a detailed annotation file. This dataset not only provides a rich resource for the analysis of infrared target characteristics but also holds significant value for research in small target detection and precision guidance. By leveraging this extensive and substantial dataset, we are able to evaluate the performance of our algorithm under varying levels of noise, atmospheric conditions, and other real-world variables. These experimental results contribute to a more comprehensive demonstration of the effectiveness and adaptability of our design, and they also provide a robust benchmark for future research in related fields, as shown in Figures 11-13. Each test dataset generates four graphs. The images include the original drawing, the MATLAB R2021a double-calculation result, the simulation hardware calculation result (fixed = 11), and the actual hardware simulation result.  
Through extensive testing on the dataset, the algorithm implemented in this paper demonstrates a false positive rate of only 7.69%, indicating a high level of recognition accuracy. This algorithm is capable of reliably identifying infrared small targets across various types of backgrounds.  
Figure 11: Test data example 1. The targets are highlighted with red boxes.