To this end, a mission-chain-driven UAV swarm was first proposed by our previous study [13]. First, the UAVs equipped with high-definition cameras searched for human targets and obtained the location. Then, the bio-radar carried by another UAV was released at this location to detect the respiratory signal of the human target. The throwing method may damage the bio-radar to reduce efficiency, causing the huge consumption of SAR resources. Hence, in this paper, we use an airborne radar sensor to observe the vital signals, which is the weak displacement of human chest caused by respiratory activity. This requires a platform with good time stability. However, a new problem emerges. Owing to wind and communication quality limitation in practical application, the UAV platform may be unstable in hovering mode, which will change the location of the human target in the radar echo data. On the other hand, the micro motion signal of the human will be submerged in the platform motion. Therefore, in order to realize the effective detection of vital signals using airborne radar, it is very important to eliminate the interference of platform motion.  
To our best knowledge, several studies have made tremendous effort to conduct the issue of UAV platform motion compensation. [PERSON] et al. proposed a dual radar system: the first radar points at the human target to sense respiratory motion, and the second radar points at the stationary wall (ceiling) to measure the platform motion [14]. Then, the signals collected using the two radars are used as the input signal and the reference signal of the adaptive filter, respectively, to eliminate platform motion interference, and the respiratory signal was successfully recovered. However, the experiment was carried out on a mechanical vibration simulator built indoors, which needs to be verified on real UAVs, and the system requires that there must be a stationary wall (ceiling) in the environment, so the application scenarios are very limited. [PERSON] et al. proposed a background residual method, in which the echo of static background (ground) is regarded as the platform movement signal and the vital signal is obtained by the phase residual between the echo signal of the human target and the echo signal of static ground. The effectiveness of the method was verified on a real UAV platform. Nevertheless, in data processing, it is difficult to automatically select the range bin where the static background is located, and when there is other interference in the ground range, the interference will be introduced into the detection results [15]. [PERSON] et al. also used a UAV as the moving platform for radar, but their study did not focus on platform motion interference cancellation [16]. In general, study on vital signal detection using airborne radar is less frequent, and it is in the stage of laboratory research. There is an enormous challenge that remains associated with the interference of the unstable UAV platform.  
In this paper, we built a UAV-mounted ultra-wideband (UWB) radar system, which hovers at low altitude to collect data from ground injured human. Then, the problem of respiration detection can be described as the problem of separating the source signals (respiration and platform motion) from the mixed observation signals of multiple range channels. To solve this problem, we proposed a signal processing method based on independent component analysis (ICA) to separate the respiratory signal. It can correctly separate the respiration of a ground human target from the noisy signals, without any supervision and prior information of platform motion. Meanwhile, it has strong noise tolerance and environmental adaptability.  
The rest of this paper is organized as follows: Section 2 describes the UAV-mounted UWB radar system; Section 3 derives the respiratory signal model with platform motion interference; Section 4 introduces the proposed signal processing method; Section Section 5 presents and discusses the results of the experiments; Section 6 draws conclusions.