# A GNSS-Cellular Network Hybridization Strategy for Robust Positioning
## 1 Introduction  
By the end of 2024, approximately 4.88 billion people owned smartphones, representing 60.42% of the global population [1]. According to the latest GSA GNSS Market Report [2], the number of smartphones equipped with GNSS capabilities is projected to increase from 6.4 billion in 2019 to 9.6 billion by 2029. Currently, 4G technology accounts for 60% of global mobile connections, and 5G is projected to reach 1.2 billion smartphone connections by 2025 [3]. In the United States, the Federal Communications Commission (FCC) [4] plays a central role in telecommunications regulation and participates in the standardization efforts led by the 3 GPP [5]. Through legislation such as the Wireless Communications and Public Safety Act of 1999 (911 Act) [6], the FCC mandated mobile emergency call infrastructure. Phase I required reporting the caller's phone number and cell tower location, while Phase II introduced more precise location data (latitude, longitude, and altitude) within 50-300 m when a GNSS is available. The updates in 2012 under the Next Generation 911 initiative further addressed indoor positioning [7]. 3 GPP develops globally standardized specifications across multiple generations of mobile networks, including GSM (2G), UMTS (3G), LTE (4G), and NR (5G) [8]. The 5G positioning architecture builds upon LTE while maintaining backward compatibility [9]. Technical specification TS 36.305 V17.3.0 defines location services (LCSs) for LTE, detailing architecture, protocols, and methods like E-CID, A-GNSS, DL-TDOA, UL-TDOA, and RTT, which rely on signal-based measurements (ToA, TDoA, AoA, RSS, etc.) [10; 11; 12]. These are further enhanced using technologies such as Wi-Fi, Bluetooth, barometric sensors, and inertial motion data. Hybrid positioning, defined in the LTE standard [10], integrates multiple methods to enhance the accuracy and reliability, particularly in challenging environments like urban canyons or indoor areas with limited satellite visibility. Hybrid methods enhance the availability, continuity, and integrity, while reducing power consumption in mobile GNSS chipsets [13; 14; 15]. They are especially valuable where 4G/5G outperforms a GNSS, such as in non-line-of-sight (NLoS) or multipath-prone environments. While 3 GPP provides a framework for hybrid positioning, it does not prescribe specific algorithms [13; 16; 17]. These are typically developed independently by mobile network providers. Moreover, since most positioning measurements are not accessible on commercial devices, research into hybridization often relies on simulations or emulated environments [16; 18; 19; 20]. Recent studies [11; 21; 22; 23] have highlighted the effectiveness of hybrid and trilateration-based approaches. In addition, more recent contributions have explored alternative fusion models and control-oriented strategies to enhance the robustness in the presence of non-Gaussian noise and outliers. For instance, [PERSON] et al. [24] presented an adaptive fusion method combining IMU and UWB data, explicitly addressing non-line-of-sight scenarios using dynamic weight adjustments and outlier suppression techniques. Similarly, [PERSON] et al. [25] proposed a localization framework based on parallel robot kinematics as an alternative to traditional trilateration, introducing geometric constraints that improved the stability under degraded conditions. These approaches offer complementary perspectives to the present user-level GNSS-cellular hybridization strategy and reinforce the importance of resilient fusion schemes in modern positioning systems. This study proposes two novel hybridization methods at the user level: position-level hybridization and measurement-level hybridization. While hybrid positioning techniques are already employed by mobile network operators within their infrastructure, the novelty of this work lies in implementing such methods directly on the user's device, without relying on network-side processing.