# Evaluating GNSS Receiver Resilience: A Study on Simulation Environment Repeatability
## 1 Introduction  
Global navigation satellite systems are deeply embedded in our world, providing seamless navigation on our phones and supporting the complex operations of maritime, aviation, and energy systems [1]. Their precise timing, derived from atomic clocks onboard satellites, enables stable time synchronization for essential services, including financial trading, broadcasting centers, and advanced decentralized networks [2, 3, 4]. With four major constellations now operational (GPS, Galileo, BeiDou, and GLONASS), the number of users relying on GNSSs for positioning, navigation, and timing continues to expand [5]. However, this widespread dependence also introduces vulnerabilities [6]. As early as the 1970s, when GPS was initially designed, engineers anticipated potential interference, both intentional and unintentional [7]. To mitigate this, they employed spread spectrum techniques and, for added security, a military signal alongside the civilian one with much greater bandwidth and thus resistance to narrowband interference [8]. For two decades, the system proved remarkably robust, with interference primarily limited to military conflict zones or unintentional disruptions from early mobile phone networks [9]. Despite the increased resilience provided by multiple GNSS constellations, the widespread availability of low-cost, portable jammers, thanks to rapid semiconductor development, has created a new vulnerability [10]. Even simple, low-power devices can effectively disable a receiver's positioning and timing functions [4]. These devices can overwhelm weak satellite signals, which are often close to the receiver's noise floor, or saturate the sensitive low-noise amplifiers of active antenna circuitry [9; 11; 12; 13; 14]. While interference can be disruptive, it is not always catastrophic, provided we recognize when a receiver's data are compromised. Researchers have developed various methods for detecting interference, ranging from monitoring amplifier gain stages to analyzing signal patterns [5; 15; 16]. Modern receivers can often identify strong jamming signals and adapt accordingly, using techniques like adaptive filtering or switching to alternative GNSS systems [17]. More subtle forms of interference, however, pose a greater challenge. These low-level jamming signals, which might originate from distant sources or be deliberately transmitted by malicious actors, can degrade positioning and timing accuracy without triggering automatic jamming detection mechanisms [18; 19; 20]. This allows them to disrupt operations without causing a complete system failure. The escalating threat of GNSS jamming has seen a dramatic surge, with a staggering increase of over 500% in reported incidents during the second quarter of 2024 [21]. This spike, particularly prevalent among aircraft operators near conflict zones experiencing widespread GPS jamming, has yet again propelled studies of interference impacts on GNSS receivers to the forefront of research. While some investigations employ partial simulations, the majority of researchers opt for real-world observations [9; 10; 12; 14; 22]. However, these field studies are inherently limited by the dynamic nature of the environment. Factors such as fluctuating satellite positions, varying atmospheric conditions, and unpredictable ionospheric activity make repeatable measurements exceedingly difficult [23]. Consequently, the conclusions drawn from these observations often lack generalizability, as they are confined to specific scenarios and cannot be independently verified [24]. Furthermore, the variability in receiver software responses adds another layer of uncertainty to the analysis [25; 26]. This research addresses the critical need to quantify the uncertainty in GNSS receiver software behavior under interference, particularly when operating near critical thresholds. By utilizing physical GNSS simulators under controlled laboratory conditions, we aim to overcome the limitations of real-world observations [27].