## 8 Discussion  
In view of the increasing air traffic, especially with regard to the integration of UAVs into the airspace, as well as increasing urbanization, methods for improving airspace safety are playing an ever greater role. Hence, the development of the ACAS X family started because it supports onboard processing for large as well as unmanned aerial vehicles to create a predictive situation map and to contribute to route optimization [6; 8]. Towards this goal, a multitude of sensor signals will be combined. In addition, many developments have been initiated as part of 6G initiatives, particularly for small aerial vehicles like UAVs, since 6G targets 3D networks [13], i.e., integrated space-air-ground networks [9; 10; 11; 12; 14]. We use ACAS X recommendations as a baseline for our setup, but our work is neither restricted to this ATC system nor to 6G research. Again this background, reliable high-rate data transmission and radar technology are becoming increasingly important. Joint communication and sensing is currently an important area of research [33; 34; 51; 52; 53]. However, this also implies the need for suitable antennas that can be integrated into UAVs in terms of weight, size and design, and radiation characteristics. In numerous preliminary studies, we proposed the use of M\({}^{3}\)PA antennas for this use case. In this research paper, for the first time, we present measurement results from an anechoic measurement chamber for the ATC scenario under investigation, supported by computer simulations. Numerical results, experimental setup, and measurement results are reported in Section 5, Section 6, and Section 7, respectively. The numerical results proved the ability of the cuboid M\({}^{3}\)PA in combination with the presented MUSIC-based algorithm to theoretically perform accurate DoA estimation. The practical verification was provided by means of two experiments employing a cuboid M\({}^{3}\)PA prototype. In the first experiment, test signals from known directions were transmitted through a horn antenna and received via the M\({}^{3}\)PA under investigation. As a measure for the estimation error, the error angle \(\psi\) was taken into account, since it provides an intuitive 3D measure of the error of the estimated direction in the 3D spherical space. It can be seen from Table 1, with the exception of the lowest transmit power level, i.e., \(P_{\mathrm{T}}=-50\) dBm, that the investigated M\({}^{3}\)PA delivers a quite good DoA estimation performance given a wide range of SNR scenarios. The DoA estimation error is commonly less than \(5^{\circ}\) for signals arriving from directions with \(\theta<90^{\circ}\). For signals arriving from directions with \(\theta=90^{\circ}\), the error could be larger the \(5^{\circ}\), yet it remains much smaller than the tolerated error of \(30^{\circ}\) in the currently in-use ACAS II [15]. Hence, the radiation pattern of the M\({}^{3}\)PA under investigation fulfills the ACAS Xu standard requirements [36]. In order to gain deeper insight into the results of this experiment, the mean and the standard deviation of the error angle \(\psi\) for MUSIC-based DoA estimation were compared to optimal ML DoA estimation. The comparison shown in Figure 7 takes signals arriving from directions with \(\theta<=90^{\circ}\) into account. The highly similar results of this comparison indicate a near-identical performance in both methods. Based on the design of the investigated cuboid M\({}^{3}\)PA shown in Figure 1 and its mounting on the body of an aircraft according to Figure 2a, the majority of the radiated power is focused towards the upper hemisphere, see Figure 2b-d. For signals arriving from directions underneath the horizon, i.e., \(\theta\leq 90^{\circ}\), the performance of the DoA estimation naturally degrades, see Table 1. However, collision avoidance systems typically rely on two antennas for interrogations. One is mounted on the top and the other on the bottom of the aircraft body [36; 41].