###### Abstract  
This study involved channel modeling and characteristics analysis of unmanned aerial vehicles (UAVs) according to different operating trajectories. Based on the idea of standardized channel modeling, air-to-ground (AG) channel modeling of a UAV was carried out, taking into consideration that both the receiver (Rx) and the transmitter (Tx) ran along different types of trajectories. In addition, based on Markov chains and a smooth-turn (ST) mobility model, the influences of different operation trajectories on typical channel characteristics--including time-variant power delay profile (PDP), stationary interval, temporal autocorrelation function (ACF), root mean square (RMS) delay spread (DS), and spatial cross-correlation function (CCF)--were studied. The multi-mobility multi-trajectory UAV channel model matched well with actual operation scenarios, and the characteristics of the UAV AG channel could be analyzed more accurately, thus providing a reference for future system design and sensor network deployment of sixth-generation (6G) UAV-assisted emergency communications. multi-trajectory; multi-mobility; UAV-assisted emergency communications; Markov chain 20232335720233/10/05/002323/10/05/023232572023/10/05/0232323/10/05/0232323/10/05/0232323/10/05/02323/10/05/0232323/10/05/0232323/10/05/0232323/10/05/0232323/10/05/0232323  
article [7], and some typical channel characteristics--such as temporal autocorrelation function (ACF), delay power spectral density (PSD), spatial cross-correlation function (CCF), angular PSD, and frequency correlation function (FCF)--were studied. Based on the spatial and temporal correlation of the MIMO channels of an HST, a spatial-temporal channel prediction model, introducing a convolutional neural network (CNN) and convolutional long short-term memory (CLSTM), was presented [8]. For the purpose of meeting the demands of future terahertz-(THz)-band communications in smart HST scenarios, the related channel characteristics for train-to-infrastructure (T2I) were analyzed, based on ray-tracing (RT) simulators and actual measurement data [9]. The performance of THz communications under different antenna patterns and weather conditions was investigated, based on channel capacity, in article [10]. Waveguide effects at the sea surface became a research focus topic when the research area shifted to maritime channel modeling. A non-stationary channel model for 3D UAV-to-ship was proposed in article [11], considering the line of sight (LoS) component, the single bounce (SB) component due to sea surface fluctuations, and the multiple bounce (MB) component due to sea surface waveguide effects. The rapid advancement of UAVs has placed them in an advantageous position in auxiliary communications, due to their high mobility and ease of deployment [12]. A UAV-assisted search and location strategy was proposed, to locate victims in disaster non-LoS (NLoS) situations [13]. Article [14] proposed a UAV-to-vehicle (U2V) mmWave communication channel model, to research the relevant channel characteristics. For vehicle communications in harsh environments, a UAV as a mobile base station (BS) can achieve better wireless services [15]. At present, there are two main methods for the research of the UAV AG channel: one is based on an empirical channel model according to measurement data; the other is based on a deterministic or geometric channel model according to simulation data [16]. To better meet the needs of future 6G application communications, the measurement scenarios of UAV AG communications are mostly focused on cities, suburban areas, rural areas, and open areas. Some key statistical properties, such as large-scale fading or small-scale fading, have been analyzed in these areas [17].