Equipped with inter-satellite links (ISLs), BeiDou-3 satellites are able to operate autonomously in case of ground facility malfunction. To provide reliable positioning, navigation and timing service during autonomous operation, satellites need to maintain a stable time reference. In this letter, we propose a minimal covariance distributed Kalman filter (MCDKF) to realize distributed timekeeping. The proposed algorithm overcomes the problem of network dynamics. Meanwhile, it improves the stability of the on-board time reference by minimizing the variance of the estimate errors at each update step, under the constraint of local observations. Simulation results show that the proposed algorithm reduces the estimation variance and improves the frequency stability compared to other distributed algorithms.

The paper proposes an open and shared sustainable mega-constellation with the “cloud-pool-terminal“ paradigm and “Sensors+Network+AI” architecture, reshaping the satellite service mode and effectively solving the challenges of space environment sustainability.

A high-precision time reference is fundamental to the positioning, navigation, and timing (PNT) of global navigation satellite systems (GNSS). The precision of clock steering determines the accuracy of practical applications that rely on the time–frequency reference. With the invention of direct digital synthesizer (DDS) technology, digital clock steering (DCS) has gradually become a mainstream technology. However, the key factor limiting DCS accuracy is the system quantization noise, which leads to a low frequency and phase adjustment accuracy. Here we propose a DCS method based on $\Sigma$-$\Delta$ modulation to address the issue of low resolution of DAC through shaping the quantization noise. A simulated GNSS time–frequency reference system experimental platform is constructed to validate the effectiveness of the proposed method. The experimental results demonstrate that this method achieves a phase adjustment accuracy of 0.48 ps and a frequency adjustment accuracy better than 0.48 pHz, which is two orders of magnitude higher than that of existing GNSS time–frequency reference systems. Thus, the proposed method offers a significant improvement in time–frequency reference systems, leading to better performance, reliability, and accuracy in a wide range of practical applications.

In complex low-altitude environments, unmanned aerial vehicles (UAVs) require reliable positioning, yet Global Navigation Satellite System (GNSS) signals are vulnerable to occlusion and interference. Pseudolite-augmented cooperative localization, which combines ground base-station signals with inter-UAV relative observations, can complement GNSS in such environments. However, two practical issues remain in real-world deployment: UAV-to-base-station (U-B) and UAV-to-UAV (U-U) observations have markedly different error statistics that a unified noise adjustment cannot handle, and the conservative covariance estimates produced by Covariance Intersection (CI) fusion bias the innovation-based adaptive noise estimation in distributed architectures. To address these issues, this paper proposes a Distributed Group Covariance Compensation Adaptive Kalman Filter (DGCC-AKF) for collaborative enhancement of UAV regional localization. DGCC-AKF establishes a group adaptive mechanism that independently adjusts the noise covariance matrices of U-B and U-U observations, enabling observation-type-level adaptive weighting that suppresses anomalous U-B or U-U measurements at the group level. In addition, a bounded covariance compensation factor is incorporated to alleviate the CI-induced conservatism in the adaptive noise estimation. The proposed method is evaluated on a 2800 km² semi-physical testbed based on the Ground-based High-precision Local Positioning System (GH-LPS) pseudolite network using measured U-B observations and high-dynamic (>300 km/h) flight trajectories collected from a fixed-wing platform across three independent flight sessions. Results demonstrate that under observation fault periods, the proposed method improves 3D positioning accuracy by up to about 75% over single-UAV extended Kalman filter (EKF). Compared with two advanced algorithms in this field, variational Bayesian adaptive Kalman filter (VBAKF) and maximum correntropy criterion Kalman filter (MCC-EKF), it is the only scheme that remains accurate and stable across all UAVs and fault types. The framework provides a practical step toward field deployment for resilient multi-UAV cooperative navigation in pseudolite-augmented GNSS-denied regions.