# Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management
## 2 Review of the State-of-the-Art Technology
### Detection by Data Fusion
This process should ensure that only measurements that refer to the same drone are associated. There are different ways to perform this process: one of them is by spatial synchronization, i.e., seeing that a pair of measurements from different sensors have very similar position values. The coordinate's changes, bias estimation and correction are sources of errors to be considered in this process. Furthermore, before making any kind of association, it is necessary to make a time synchronization so that all the measures refer to the same instant of time. The last problem faced by data fusion systems is filtering and prediction, for which they usually use common techniques such as Kalman filtering and Bayesian methods. A low-cost, low-power methodology consisting of a fusion of technologies linking several sensors is presented in [45]. This technology includes a simple radar, an acoustic array of microphones and optical cameras that are used to detect, track, and discriminate potential airborne targets. The multimode sensor fusion algorithms employ the Kalman filter for target tracking, and an acoustic and visual recognition algorithm is implemented to classify targets. The first element of the multimode sensor network is the radar, which is responsible for detecting targets that are approaching the area of interest. The second component is the acoustic microphone array, whose main objectives are to provide target arrival direction and target identification and classification and to mitigate false alarms. The last sensor is the optical system composed of infrared detectors to improve the resolution of targets. Results show that this sensor fusion is useful for detecting, tracking, and discriminating small UAVs. Another set of heterogeneous sensors combined with a sensor data fusion is proposed in [46]. This system is composed of a Radio Frequency (RF) sensor to capture the uplink and downlink communications of the UAV, an acoustic sensor searching for the rotor noise, a passive radar system using the cellular network and a multihypothesis tracking (MHT) system for the fusion of sensor data. Finally, in the case explained in [47], the system is composed of different range acoustic, optical and radar sensors. There is a combination of sensors of long- and short-range detection, the passive RF receivers detect the UAV's telemetry signals, and the camera and microphone sensors are used to increase the detection accuracy in the near field. Specifically, the system is composed of a 120-node acoustic array that uses acoustic signal to locate and track the UAV; 16 high-resolution optical cameras, which are used to detect the UAV in the middle distance; and MIMO radar (with three bands) to achieve remote detection in the long distance. The developed combination overcomes the drawbacks of each of the sensor types in UAV detection and maximizes the advantages of the sensors. At the same time, the system reduces the cost of large-scale sensor deployment. In this paper, we focus on the simulation of individual sensors, so we do not simulate these integrated solutions, which remains an area for future research, especially for the cases in which some of the sensors are controlled by the outputs provided by others. Regarding commercial solutions, some of them are based on integrating some of the previously described sensors. For instance, a commercial solution provided by Indra [13], called ARMS (Anti-RPAS Multisensor System), is a multilayer system ready to support the full C-UAS cycle, combining multiple types of sensors and countermeasures, ready to be deployed in different formats (fixed, mobile, portable) and designed to interact with complementary systems in to provide defense against UAVs threats. It is composed of a radar (described in Section 2.1), a jammer (to interfere with drone control or GPS navigation) and optronics (described in Section 2.5). HENSOLDT Xpeller Counter UAV solution [14] combines various types of sensors and effectors for protection against small drones.