## 5 Discussion  
Based on suspected target detection experiments under two typical _Scenarios_, experimental results above clearly show that all suspected targets could be accurately detected and separated from the background based on multispectral feature information only (with only an additional detection mistake), even under a complex outdoor bush environment. However, the study also amply demonstrates that the human attributes of the suspected target cannot be confirmed and verified based on unimodal information alone. Furthermore, combined with auxiliary physiological information from the bio-radar, only the true human target in _Scenario 2_ was accurately identified. Therefore, the bimodal-information-based human target recognition method proposed is initially proven to be effective for ground injured human subject search in an outdoor environment. For this challenging identification task, the recognition is satisfactory to some extent. However, this method also has a few limitations for practical application in the future.  
With respect to the initial suspected target detection, the recognition algorithm adopted in this experiment is the decision tree in supervised learning. Objects are classified by different properties thresholds, which is conducive to real-time processing. Meanwhile, it also leads to the problem that the robustness of the classification method is weak because we cannot conclude spectral of all the clothes and find appropriate thresholds. Therefore, our future research work will focus on superior target features with higher stability and more intelligent and excellent recognition algorithms, such as deep learning recognition algorithms. In addition, reducing the time complexity of the model and more robust target recognition-based multimodal optoelectronic information fusion are also our future research directions.  
On the other hand, just as what we tried in this paper, the bio-radar is applied to acquire the vital signals, which can help us to make the judgment whether the subject is human or not and to infer the state of life. However, the detection environment and condition are totally different and even more challenging than that of our previous indoor vital sign detection in the medical field [35], and survivors detection under the ruins in the post-disaster search-rescue field [7]. (1) Firstly, the effective detection range of the micro-bio-radar is only 10 m, so finding a way to throw the bio-radar from the UAV platform to a proper location around the suspected target to acquire vital signals is still a challenging job. Secondly, the problem of \"differentiation between human and non-human (animal) live targets\" and \"respiration rate varies in different physiological conditions\" are inevitable issues to consider in the outdoor detection environment. Fortunately, our past studies have tried some very similar exploring experiments [12, 36, 37]. These studies confirmed  
Figure 16: Radar echo of human target T\({}_{12}\) from 8 m and corresponding frequency analysis, (**a**) original signal and normalized frequency spectrum, (**b**) noise cancellation signal and normalized frequency spectrum, (**c**) human target reconfirmation result.  
that some differences can be observed between human body respiration and that of other animals (cats, dogs, pigs, etc.) in the time domain, but they are not significant. Nevertheless, the human and other non-human (animal) live targets can be accurately differentiated based on multi-domain features (time domain, time-frequency domain) combined with some machine learning method. Therefore, we will try to embed these algorithms into the UAV bio-modal system and improve it by carrying out some test experiments in our future work. And for the problem of \"the same subject's respiration varies in different conditions\", it can be solved by similar ideas.