## 1 Introduction  
New solutions for navigation and positioning are deemed essential for autonomous driving applications [1]. The Global Navigation Satellite System (GNSS) is the most common tool for this endeavor; however, the position error of modern systems is 30 cm in best case scenarios [2], not reaching the under 10 cm error requirements for fully autonomous driving [3]. Depending on the environment, it may be difficult to evaluate the position due to signal degradation, blockage, or interference [4]. The antenna is a key component of positioning systems, but traditional antennas are static, which means they cannot adapt to the surrounding environment, which can be very dynamic (e.g., a car traveling through a city where multipath signals are abundant) [4]. While multipath signals are an interference that happens due to the interaction between GNSS signals and the environment, there are also man-made interferences that can intentionally disable or trick the GNSS positioner. These interferences are called jamming and spoofing. Jamming consists of broadcastingsignals with the same characteristics as GNSS satellite signals, but at much higher power levels, degrading the carrier-to-noise ratio of the GNSS signals and making it impossible for the GNSS receiver to acquire them. On the other hand, spoofing consists of generating fake GNSS signals with navigation data that are interpreted by the GNSS receiver, tricking it into generating an erroneous positioning and timing solution without awareness [5]. As it becomes apparent, for fully autonomous driving solutions to be safe and reliable, it is of paramount importance to grant positioning systems resilience against these interferences. Satellite signals come predominantly from above the antenna (zenith). However, the antenna can also receive multipath signals coming from high- or low-elevation angles, as illustrated in Figure 1. These are due to signals reflecting on the ground, on buildings, or on geological formations. Additionally, man-made interferences such as jammers can also be present. Jamming signals typically come from low-elevation angles [6]. Choke ring antennas have concentric conductive walls that reflect signals coming from low-elevation angles, thus reducing the antenna gain in those directions. They have been proposed as solutions to effectively attenuate the unwanted interfering signals [7; 8]. However, these antennas are static and cannot adapt to a highly dynamic environment such as the ones where autonomous vehicles will need to operate. Additionally, suppose the interferences come from higher elevation angles (such as the top of a tall building or multipath signals in an urban canyon). In this case, the efficacy of the choke ring antenna on ignoring these signals is compromised. With the use of an antenna array, it is possible to dynamically adjust the radiation characteristics of the antenna, allowing for spatial filtering of the received GNSS signals by attenuating the multipath signals and increasing the gain of direct satellite signals [9]. Furthermore, it is also possible to identify the Direction of Arrival (DoA) of jamming or spoofing signals and dynamically mitigate them through spatial filtering [5]. The implementation of an antenna array is not, however, a straightforward task. The increased number of antennas that compose the array occupies a large area and requires a complex feeding network. A beamforming mechanism is also necessary. An analog beamformer uses phase shifters and variable gain amplifiers to control the phase and amplitude of the signals. At least one of each is required for each antenna element that composes the array, leading to complex and expensive setups for large arrays. Digital beamforming is achieved by sampling the incident RF signal at each antenna element using analog-to-digital converters (ADCs), allowing for the reduction in component count and  
Figure 1: Representation of the distinct paths GNSS signals travel before reaching the GNSS antenna.