Lan Du

Imaging based on signals reflected from global navigation satellite systems (GNSS) represents a novel approach for surface observation. Owing to the global coverage of navigation satellite constellations and the short revisit periods of satellite sub-points, GNSS reflectometry imaging enables high-temporal-resolution change detection in sensitive areas. However, stationary dominant reflectors (SDRs) in the imaging area suppress the signal amplitudes of nearby weak reflection targets, thereby increasing the difficulty of their detection. Conventional coherent change detection and differential interferometry methods demand high phase accuracy and require multiple imaging operations within the imaging domain, thereby reducing detection efficiency. To address these issues, this study proposes a method for directly suppressing SDRs directly in the range-compressed domain. This method leverages the extensive cancellation algorithm batches (ECA-B) framework and utilizes a single satellite revisit period to construct a scattering characteristic model dominated by SDRs, which serves as the reference signal for ECA-B cancellation. To ensure model stability, two-dimensional range-compressed results from multiple prior revisit periods were accumulated to construct a weighted scattering characteristic model, incorporating noise mean and variance. Simulations and real-world experiments demonstrated that the proposed method significantly attenuates signals from strong reflectors in the range-compressed domain, thereby enhancing the detectability of weak change targets.

Global navigation satellite system (GNSS) integrity monitoring relies on continuous, worldwide observation of satellite signals. While ground-based monitoring networks are constrained by geographical coverage and political boundaries, low Earth orbit (LEO) constellations offer a promising space-based alternative due to their global distribution and dynamic characteristics. Current performance evaluations of satellite constellations often rely on real or simulated ephemerides. Although the geometry and probability model (GAPM) can estimate Earth coverage without such data, it cannot model the ‘inverse coverage’ of LEO satellites monitoring high-altitude GNSS satellites. To address this gap, we propose the LEO GAPM, which explicitly models this ‘inverse coverage’ by introducing the concept of relative inclination and reformulating the observation probability function. This model estimates key performance metrics, such as monitoring coverage fold and satellite geometry dilution of precision (SGDOP), using only a small set of orbital parameters. Validation experiments show that the average difference in estimated coverage fold between LEO-GAPM and orbital propagation methods is less than 0.14, and the underestimation rate of SGDOP ranges between 5% and 13%. The model was further employed to analyze two representative LEO constellations (Iridium NEXT and CENTISPACE) for BDS-3 monitoring, assessing how coverage capability varies with the number of LEO satellites. This analysis demonstrates that constellation configuration, orbital inclination, and satellite count are key determinants of monitoring performance. Additionally, the study quantifies the number of satellites required to achieve an average coverage fold of 3–10. Overall, our study provides a computationally efficient tool for preliminary design and resource allocation in space-based GNSS monitoring systems.

To address the stringent autonomous navigation requirements in cislunar space, this study proposes a hybrid constellation navigation framework utilizing inter-satellite links. It integrates near-rectilinear halo orbits (NRHOs), distant retrograde orbits (DROs), and BeiDou system (BDS) geostationary orbit/inclined geosynchronous orbit satellites into a dual-layer network, employing Ka-band dual one-way ranging and an extended Kalman filter for real-time state estimation. Monte Carlo simulations under high-fidelity dynamics reveal that: Without BDS augmentation, NRHO and DRO satellites achieve 100 m-level 3D accuracy with 5 d arcs, improving to 10 m level with 10 d arcs, and reaching sub-meter (DRO) and meter-level (NRHO) with 15 d arcs. Symmetric NRHO configurations show poor short-arc performance due to geometric correlation. The hybrid constellation with BDS augmentation significantly outperforms standalone lunar constellations, achieving 20–30 m 3D accuracy with just 2 d arcs. The key contribution is the synergistic integration of heterogeneous orbits, leveraging NRHO’s lunar proximity, DRO’s wide coverage, and BDS’s stable Earth reference to enhance autonomy, accuracy, and robustness. This framework reduces ground dependency and provides a scalable solution for future cislunar exploration.

An experimental analysis method for the correlation peak distortion of the BeiDou‐3 satellite navigation system (BDS‐3) B1C signal is proposed to address the gaps in existing research—lack of experimental validation and insufficient insights into tracking loop distortion mechanisms under spoofing. First, the autocorrelation characteristics of the B1C signal under narrow band (NB) and wide band (WB) tracking modes were analysed based on its modulation characteristics. A feeder‐based BDS spoofing experimental platform was constructed to accurately simulate real‐world spoofing scenarios and collect high‐fidelity intermediate frequency (IF) data. Multi‐correlator tracking outputs were utilised to quantify the distortion effects of spoofing types and tracking modes on the B1C tracking loop. Experimental results revealed three key findings: (1) during spoofing takeover, the combined correlation peak oscillates at high frequency (spoofing as reference, residual true signal as amplitude); (2) under position spoofing, correlation peak pulling direction/rate vary by satellite; under time spoofing, the pulling is consistent across satellites; (3) WB tracking offers higher ranging accuracy than NB tracking but is more susceptible to spoofing‐induced pulling and exhibits a more complex correlation peak distortion. These results provide a theoretical and experimental basis for the design of BDS‐3 anti‐spoofing algorithms and equipment optimisation.

The growing demand for lunar exploration and infrastructure development requires reliable, real-time navigation for spacecraft in cislunar space, particularly those in Distant Retrograde Orbits (DROs) and Near-Rectilinear Halo Orbits (NRHOs). However, Global Navigation Satellite Systems (GNSS) signals are weak at lunar distances, with few visible satellites, high Position Dilution of Precision (PDOP), and low carrier-to-noise ratios (100 meters). Current augmentation strategies, such as deploying active lunar satellites, face challenges in deployment complexity and sustainability. This study proposes a passive Position, Navigation, and Timing (PNT) framework that combines lunar and terrestrial beacons to establish a robust spatial reference network. The system leverages dual-anchor signals from both Earth and the Moon to exploit spatial diversity and geometric redundancy, overcoming GNSS limitations. We developed mathematical models for pseudorange measurements, DOP metrics, and positional estimation errors. Over a 30-day simulation, we evaluated three PPNT models (PPNT1: Earth-based only; PPNT2: Earth with a nearside lunar station; PPNT3: Earth with additional lunar polar and farside stations) in terms of geometric visibility, PDOP, C/N0, and receiver noise error. Results show that PPNT3 significantly outperforms other methods due to its geometric configuration, reducing PDOP by 45.9%-54.3%, achieving centimeter-level receiver noise error, enhancing stability, and providing the highest carrier-to-noise ratio. This research bridges the gap between terrestrial PNT technologies and cislunar mission requirements, offering a novel pathway to improve real-time positioning accuracy in weak GNSS environments. It advances the PNT ecosystem, supporting future lunar infrastructure and deep-space exploration through scalable, passive navigation solutions.

To address the limitations of traditional navigation systems in lunar exploration missions regarding positioning accuracy and coverage, this study proposes a multi‐objective optimization framework for the Lunar Navigation Satellite System (LNSS) to support the lunar re‐exploration mission. A comprehensive simulation system encompassing the entire mission process—powered descent, landing, and surface operations—was developed, integrating a hybrid‐orbital architecture (DRO, ELFO, and NRHO) and a multidimensional navigation performance evaluation model. An improved genetic algorithm (IGA) was employed to optimize the constellation configuration under constraints including satellite count ( N = 9), geometric dilution of precision, signal attenuation, and cost (75% reduction compared to full‐scale architectures), yielding the optimal D1E4N4 configuration. Experimental results demonstrate that the optimized LNSS achieves continuous four‐satellite visibility during critical mission phases (landing preparation, powered descent, and surface exploration). The system effectively mitigates signal occlusion caused by complex lunar terrain. These findings provide critical technical support for China’s crewed lunar missions and the International Lunar Research Station (ILRS), advancing the transition of Earth–Moon navigation systems from theoretical validation to practical implementation and laying a foundation for sustainable deep‐space exploration.