# Multi-UAVs Communication-Aware Cooperative Target Tracking
## 1 Introduction  
In recent years, UAVs (Unmanned Aerial Vehicles) are playing an increasingly important role in collaborative investigation, battlefield combat, target status monitoring and other fields [1; 2; 3]. One of the most popular area is the multi-UAVs cooperative target tracking problem which can be abstracted as a mobile sensor network configuration problem [4; 5; 6; 7; 8]. The optimal sensor configuration can greatly reduce uncertainty of target state estimation. In the traditional target tracking problem, communication between sensors (or UAVs) usually uses the disc model [3], which means that the UAV has a clear communication radius. But the real communication between UAVs is very complicated. The disc model is only an ideal model, which cannot be a good simulation model of real communication. There have been some researches on target tracking problem with communication awareness [9; 10; 11; 12], but the communication models ignore the impact of interference and attenuation. In reality, quality of communication is affected by many factors. There are many characteristics of the wireless channel of networked UAVs, such as signal-to-noise ratio (SNR) [9; 11; 12], signal-to-interference-plus-noise ratio (SINR) [13; 14], and throughput gains [15; 16; 17; 18; 19]. SINR is widely used to describe the quality of a transmission in presence of interference. In order to improve the network performance using UAVs as flying base stations in a device-to-device communication network, SINR model was used in Reference [13] to quantify communication quality. In Reference [14], the authors studied a cellular-enabled UAV communication system consisting of one UAV and multiple GBSs (ground base stations). The quality of GBS-UAV link was determined by the received SNR. A similar problem was also studied in Reference [15], where an UAV was dispatched as a mobile AP (access point) in an UAV-enabled wireless powered communication network. The work in Reference [16] considered a new method maximizing the minimum throughput over all ground users to optimize UAV trajectoryin a wireless network. In Reference [17], to optimize the performance of UAV-based wireless systems in terms of the average number of bits transmitted to users, a framework was proposed to get a better communication between UAVs. In Reference [18], a novel framework was proposed to enable reliable uplink communication for IoT (Internet of Things) devices with a minimum total transmit power. The authors in Reference [19] proposed a new hybrid network architecture by leveraging the use of UAV as a mobile base station to help offloading traffic from GBSs. In these works, the quality of communication was essential and well modeled. However, it was difficult for us to use these models in the cooperative target tracking problem since we required to reach a compromise between observation and communication. The method proposed in Reference [9] raised a communication probability model calculating the probability of successful information transmission between UAVs according to the signal-to-noise ratio. This probability and information was used to calculate UAV expected information, which could be used as criterion for maneuver decision-making. This probability model could partly reflect the influence of UAVs communication on expected information, but could not reflect real conditions of communication. In reality, information transmission is not just successful or not; there may be interference from the environment or other UAVs. In order to describe the communication in a better way, the concept of \"communication noise\" was raised in Reference [11]. However, the observation model in Reference [11] was overly simplified. In addition, UAVs need to predict link qualities, which may be not practical in the real world because of deep fades. In this paper, we use the concept of communication noise and propose a new method to get control input for each UAV. The results based on communication probability model and communication noise model in this paper are compared.