# Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management
## 3 Counter-UAS Sensors Modeling  
Modeling and simulation tools are a useful alternative to test and assess the performance of complex systems in a cost-effective manner. Regarding the usage of such tools to evaluate UTM systems, authors have already proposed in [3] a simulation platform that aims to replicate drone operations and complex scenarios. The objective of the platform is to easily perform system-level evaluations of UTM. To do so, the platform simulates the required input information for UTM systems both in preflight (operation definition submission for authorization) and in-flight phases (telemetry messages from drones or tracks from surveillance networks). Thus, starting from a user-defined simulation scenario (which might include the occurrence of unexpected events or contingencies), the platform is able to replicate the behavior of the actors involved in a drone operation. Then, it forwards the required data streams to the UTM system under evaluation and can retrieve the resulting output information to carry out tests and generate evaluation metrics. This operation is schematically represented in Figure 1.  
The platform follows an agent modeling approach where the behavior of drones, ground control stations, surveillance networks and communication networks linking all agents is individually modeled. The complete behavior of the overall scenario arises from the autonomous interaction of these individually modeled agents. The environment in which drones operate is also simulated including terrain, weather, or airspace constraints. With this approach, the platform can currently simulate drone trajectories or effects such as navigation errors, communication disturbances (i.e., latencies, package losses... ) and drone detection from sensors. A model-agnostic, extendable microservices-based architecture has been used to implement the platform, as depicted in Figure 2. The architecture allows for defining multiple \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Operational Conditions** & **Range** & **Cost** & **Measures Provided** \\ \hline Active radar & Partially affected by weather conditions & Long-range (\(\sim\)5 km) & High-cost & Range, azimuth, elevation \\ Passive radar & Partially affected by weather conditions & Long-range (\(\sim\)5 km) & Low-cost & Range, azimuth, elevation \\  & Affected by RF & & & \\ Radio frequency sensor & interferences and partially by weather conditions & Medium-range (\(\sim\)2 km) & Low-cost & Azimuth, elevation, classification \\ Acoustic sensor & Affected by weather/noise conditions & Short-range (\(\sim\)500 m) & Low-cost & Azimuth, classification \\ Camera sensor & Affected by weather conditions, day/night & Medium-range (\(\sim\)1 or 2 km) & Low-cost & Azimuth, elevation, classification \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison of UAV sensing technologies. Figure 1: Operation of the simulation architecture proposed in [3]. Extracted from [3], with permission. simulation models for each agent that can be easily implemented and simultaneously simulated. The simulation of each agent is isolated within a separate microservice so that modeling changes in each service do not affect the rest of the platform. It also provides utilities to define replicable simulation scenarios where the simulated agent's specification can be defined together with the selected model to carry out their simulation. A set of simple simulation models for each agent was initially provided, as described in [3]. Particularly, a simplistic technology-agnostic model for noncooperative sensors was already provided. This model just considered a maximum range for each sensor following a pass-not pass approach. It also included a constant additive gaussian noise to model detection inaccuracies. The models proposed in this section for different technologies aim to improve that simplistic model by designing more accurate models that are based on the inner operation of each sensor type.