## 1 Introduction  
Global Navigation Satellite Systems (GNSSs), as the most widely used positioning, navigation, and timing (PNT) solution [1], exhibit some inherent limitations [2]. The most critical limitation of GNSS signals is the relatively high orbital altitude (greater than 19,000 km) [3], which induces substantial signal path loss, resulting in low minimal received power (\(-155\)dBW--\(163\)dBW) [4]. This inherent limitation leads to significantly degraded positioning performance or complete service outages in blocked environments such as urban canyons, dense foliage, tunnels, and indoor spaces [5]. Signal of opportunity-based (SOP-based) positioning has emerged as a promising alternative due to its non-cooperative texture, flexibility, and ubiquitous coverage. Recent studies have explored diverse signal sources, including amplitude modulation/frequency modulation radio broadcasts [6], Wi-Fi [7], fifth-generation cellular networks for mobile phones [8], digital television transmissions [9], and low Earth orbit (LEO) satellite signals [10]. Notably, LEO satellite signals have garnered extensive research attention due to their unique advantages: the presence of large constellations offering global coverage [11], rapidly evolving geometry [12], enhanced ground-received power [13], and pronounced Doppler frequency shifts [14]. Previous studies have achieved positioning accuracy ranging from meters to hundreds of meters by leveraging SOPs from single or multiple LEO satellite constellations [15; 16; 17; 18]. LEO SOP positioning utilizes satellite position and velocity information derived from ephemeris extrapolation as spatiotemporal references, combined with Doppler frequency and timestamp measurements extracted from LEO signals to achieve positioning. However, LEO-SOP-based positioning also suffers from inherent limitations. At the satellite level, the error of Two-Line Element (TLE)-based orbit extrapolation models grows over time [19]. The geometric configuration of satellites, characterized by their elevation and azimuth angles, directly influences the Geometric Dilution of Precision (GDOP). A lower GDOP value, resulting from favorable satellite distributions, enhances positioning accuracy by reducing the sensitivity of results to observational errors. Conversely, an unfavorable configuration (e.g., satellites clustered in a narrow angular range) elevates GDOP, amplifying error propagation. At the epoch level, the LEO SOP positioning depends on capturing numerous epochs. The introduction of numerous similar epochs from the same satellite may lead to a near-singular observation matrix, resulting in severe degradation of positioning accuracy. At the signal level, the signal-to-noise ratio (SNR) and Doppler frequency exhibit severe fluctuations [20]. These inherent drawbacks in multiple levels of LEO SOPs limit positioning accuracy. To mitigate the aforementioned inherent limitations of LEO opportunistic signals, significant research efforts have been made. In 2023, an Orbit Error Compensation and Weighting (OECW) method was proposed to compensate for Doppler frequency errors caused by orbital inaccuracies at each epoch, achieving a 70% improvement in horizontal positioning accuracy [21]. In 2024, a closed-loop machine learning model for LEO satellite orbit prediction was proposed, effectively reducing predicted orbital errors [22]. Compared to orbits predicted using the Simplified General Perturbations Version 4 (SGP4) model, the proposed model-based prediction improved LEO SOP positioning accuracy by 84.6%. In 2023, a comprehensive analysis was conducted on the influence of baseline in LEO SOP differential Doppler positioning [23]. The study achieved a 3D positioning accuracy of 122.1 m for long-baseline scenarios and 1.9 m for zero-baseline cases. At the satellite level, extensive research has been conducted on multi-constellation fusion to optimize the limited geometric configurations inherent in single-constellation systems.