In recent years, Low Earth Orbit (LEO) satellite Signals of Opportunity (SOP) positioning technology has emerged a significant alternative to the Global Navigation Satellite System (GNSS), effectively addressing navigation and positioning challenges in GNSS-denied environments. Among various LEO constellations, Iridium NEXT has garnered considerable attention due to its well-developed constellation architecture and extensive coverage. To achieve high-precision positioning using Iridium NEXT SOP, it is crucial to fully analyze both time-domain and frequency-domain observation data. Existing time-frequency joint estimation methods for Iridium NEXT signals typically employ a grid-based search strategy, which suffers from low accuracy and high computational complexity. Additionally, the high dynamic of LEO satellites introduces frequency shifts that significantly degrade bit synchronization accuracy and reduce the precision of signal estimation. To address these challenges, this paper proposes a joint high-precision frequency and code phase estimation algorithm. By incorporating a phase adjustment strategy during the bit synchronization stage, it mitigates the impact of satellite dynamics on signal estimation, enhances frequency estimation accuracy, and provides a precise synchronization sequences for code phase estimation. Additionally, leveraging the Early Minus Late Amplitude (EMLA) principle, the algorithm achieves efficient and accurate code phase estimation. It further integrates Frequency of Arrival (FOA) measurements for positioning, ensuring the effective utilization of SOPs to improve positioning accuracy. Experimental signal analysis demonstrates that the proposed algorithm achieves a frequency measurement error of approximately 1Hz, approaching the Cramér-Rao Lower Bound (CRLB). For both long-frame and short-frame signals, the accuracy is 25 m and 37 m, respectively, outperforming existing Time of Arrival (TOA) estimation methods for Iridium NEXT signals. Furthermore, in a 176m baseline differential scenario, Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) fusion positioning accuracy reaches 10m, compared to existing frequency estimation methods, the proposed approach improves FDOA-based positioning accuracy 74%.

The utilization of low Earth orbit (LEO) satellites’ signals of opportunity (SOPs) for absolute positioning and navigation in global navigation satellite system (GNSS)-denied environments has emerged as a significant area of research. Among various methodologies, tightly integrated Doppler/inertial navigation system (INS) frameworks present a promising solution for achieving real-time LEO-SOP-based positioning in dynamic scenarios. However, existing integration schemes generally overlook the key characteristics of LEO opportunity signals, including the limited number of visible satellites and the random nature of signal broadcasts. These factors exacerbate the weak observability inherent in LEO-SoOP Doppler/INS positioning, resulting in difficulty in obtaining reliable solutions and degraded positioning accuracy. To address these issues, this paper proposes a novel LEO-SOP Doppler/INS tight integration method that incorporates trending information to alleviate the problem of weak observability. The method leverages a parallel filtering structure combining extended Kalman filter (EKF) and Rauch–Tung–Striebel (RTS) smoothing, extracting trend information from the quasi-real-time high-precision RTS filtering results to optimize the EKF positioning solution for the current epoch. This approach effectively avoids the overfitting problem commonly associated with directly using batch data to estimate the current epoch state. The experimental results validate the improved positioning accuracy and robustness of the proposed method.

Positioning based on Low Earth Orbit (LEO) satellite Signals of Opportunity (SOP) often relies on Doppler observations. Therefore, the accuracy of Doppler frequency measurements significantly impacts the positioning performance. Traditional frequency estimation methods for ORBCOMM satellite signals are typically implemented in the frequency domain and neglect the impact of the “frequency chirp” effect on measurement accuracy, which leads to low computational efficiency, poor noise resistance, and limited estimation accuracy. To address this issue, a high-precision frequency estimation method combining a “coarse and fine” process is proposed. In the coarse estimation process, ephemeris prior information is combined with matched filtering to effectively separate the Doppler rate, thereby mitigating the spectral broadening caused by the Doppler rate. In the fine estimation process, ORBCOMM signal characteristics are fully exploited. Single-sideband filtering is applied to improve noise resistance, followed by precise frequency discrimination of the delayed signal. Experimental results demonstrate that the proposed method outperforms the state-of-the-art “FFT + MLE” approach, achieving a frequency measurement accuracy on the order of 0.01 Hz while requiring fewer computational resources. Furthermore, this method improves estimation performance by approximately 12 dB without compromising frequency measurement accuracy.

In Global Navigation Satellite System (GNSS)-denied environments, navigation using signals of opportunity (SOP) from Low Earth Orbit (LEO) satellites is considered a feasible alternative. Compared with single-constellation systems, multiple-constellation LEO systems offer improved satellite visibility and geometric diversity, which enhances positioning continuity and accuracy. To allocate weights among heterogeneous observations, prior studies have employed the Helmert variance component estimation (HVCE) method, which iteratively determines relative weight ratios of different observation types through posterior variance estimation. HVCE enables error modeling and weight adjustment without prior noise information but is highly sensitive to outliers, making it vulnerable to their impact. This study proposes a Robust HVCE-based dual-constellation weighted positioning method. The approach integrates prior weighting based on satellite elevation, observation screening based on characteristic slopes, HVCE, and IGG-III robust estimation to achieve dynamic weight adjustment and suppress outliers. Experimental results over a 33.9 km baseline demonstrate that the proposed method attains Two-Dimensional (2D) and Three-Dimensional (3D) positioning accuracies of 12.824 m and 23.230 m, corresponding to improvements of 29% and 16% over conventional HVCE weighting, respectively. It also outperforms single-constellation positioning and equal-weighted fusion, confirming the effectiveness of the proposed approach.

As a kind of the aircraft with orbital altitudes between 400 and 2000 km, LEO satellites with the advantages of small size and low cost, play an important role in various earth observation missions. To ensure the successful completion of each mission, it is essential to obtain precise and continuous orbit information of such satellites. Currently, Global Navigation Satellite Systems (GNSS) are mainly used for this task, but the complex electromagnetic environment in space renders on-board GNSS observation data unreliable, with serious implications for the accuracy of the orbit determination. This paper therefore proposes a new GNSS Fault Detection and Exclusion (FDE) algorithm for real-time LEO satellite orbit determination. Specifically, the advanced Quasi-Accurate Detection (QUAD) based on orbit prediction model is proposed to detect GNSS anomalies according to the deterministic relationship between the errors and the observations. The validity of the proposed algorithm is proved by test results: in the case of a single GNSS satellite anomaly, the horizontal and vertical position accuracy of the proposed algorithm is improved by 71.6% and 83.2%, respectively, compared with the traditional w-test based algorithm. As the number of anomalies increases, the proposed algorithm is shown to be able to circumvent up to four fault outliers, while keeping the 3D position error lower than 10 m. Specifically, the probability of identifying outliers remains above 90%, and the 3D position error is reduced by about 4 m in the worst case of dual anomalies, compared to the iterative w-test with observation separation algorithm.

The rapid expansion of Low Earth Orbit (LEO) satellite constellations has created new opportunities for global navigation and positioning. Navigation based on LEO Signal of Opportunity (SOP) effectively complements the Global Navigation Satellite System (GNSS) owing to their high received power and wide coverage. However, most LEO satellites suffer from orbital errors of several hundred to a thousand meters, while uncertain signal transmission times further degrade positioning accuracy. Existing orbit determination approaches and differential positioning methods can improve performance but remain limited: the former rely on long-term observations, limiting responsiveness to frequent orbital maneuvers, whereas the latter experience degraded performance over long baselines due to weakened spatial correlation of orbit errors. To address these challenges, this study introduces a real-time orbit correction method based on LEO satellite SOP from multiple stations. By anchoring satellite time references to a network of ground stations, the method imposes a strong constraint on transmission time and decouples onboard spatiotemporal reference errors. Joint processing of time–frequency observations from multiple stations enables real-time orbit error correction without long-term data accumulation. Experimental validation using Iridium NEXT SOPs shows that, within a 953 km² area, the proposed method achieves two-dimensional and three-dimensional positioning accuracies of 13.8 m and 27.5 m, improving by 36% and 21.4%, respectively, compared with existing differential approaches.