Global navigation satellite systems (GNSSs) are used in numerous fields, but their vulnerability is a global problem that has yet to be solved. A promising way to effectively address this problem is by integrating navigation into emerging dense nongeosynchronous orbit (NGSO) megaconstellations. To maximize the downlink efficiency for users, an integrated satellite navigation and communication (ISNAC) framework is proposed in this work, in which a necessary number of packetized bursty downlink satellite communication signals are used directly for navigation purposes. Then, the user terminal estimates the time of arrival (TOA) of these ISNAC signals regardless of their coding and modulation type. To this end, a TOA estimation algorithm is proposed. Specifically, the user terminal first constructs a template signal with demodulated data bits to replicate the transmitted ISNAC signal and then calculates the cross-correlation function (CCF) of the template and the received signal for TOA estimation, based on which the TOA is finally estimated. The performance bound of the proposed TOA estimation algorithm is analyzed. Simulations are conducted to corroborate the mathematical analysis and The results show that the proposed algorithm outperforms other TOA estimators for comparsion by approaching the Cramer-Rao lower bound (CRLB) with a much lower computational complexity.

As an alternative positioning, navigation and timing (PNT) method, integrated Doppler positioning is important in emerging direct satellite-to-phone communication and satellite-based remote Internet of Things (IoT) systems to help locate ground terminals. In this work, we offer a fundamental characterization of Doppler positioning by proposing a new scalable and analytical Doppler positioning performance bound that can be used for fast estimation of the Doppler positioning dilution in place of the legacy position dilution of precision (PDOP) expression. Then, to maximize the Doppler estimation accuracy, a Doppler estimation algorithm based on the whole signal packet is proposed, in which the Doppler rate is also considered. Finally, based on mathematical analysis, a Doppler positioning receiver architecture is proposed. Simulations are conducted with a 288-satellite Walker- $\delta ~800$ km low-Earth-orbit (LEO) satellite constellation to corroborate the mathematical analysis. The results show that the median relative error of the proposed performance bound for Doppler positioning is less than 2.5% and that the proposed Doppler estimation algorithm outperforms other Doppler estimation algorithms for Doppler positioning and approaches the Cramér-Rao lower bound (CRLB).

Since the antennas of high-altitude spaceborne GNSS receivers are installed in the satellite nadir direction, interference from Earth poses a potential threat. For interference mitigation in high-altitude spaceborne GNSS receivers, a spatial filtering algorithm is proposed with the constrained minimum spatial mean interference power (CMSMIP) as its metric. Specifically, a spatial mean interference power matrix is conceived based on the spatial probability distribution of the interference sources. The optimization problem for spatial filtering is formulated to minimize the spatial mean interference power expressed by the introduced matrix subject to constraints on the desired signal-to-noise ratio gain. To solve this optimization problem, mathematical properties of the spatial mean interference power matrix are derived, based on which the optimal weight vector is found. The performance of the proposed algorithm is evaluated by simulation with respect to power inversion (PI) as well as minimum variance distortionless response (MVDR), showing improved performance at typical interference-to-noise ratios. The proposed algorithm is adaptive to a large number of interference signals and consistent in performance under different levels of interference power. Furthermore, it can be implemented by retrieving a lookup table of precalculated optimal weight vectors for different GNSS signal directions of arrival, which requires less than 10 MB of memory, instead of requiring frequent snapshots and matrix inversion operations as in conventional spatial filters.

This letter addresses the problem of distributed cooperative localization using the joint range and angle measurements. With the appealing properties of fully distributed implementation and low complexity at each agent, a convex formulation named fast iterative parallel projection method (FastIPPM) is exploited to solve the cooperative localization problem. In pursuit of faster convergence, gradient descent with momentum is utilized. Simulation results validate the superior performance of the proposed algorithm compared with existing ones. After adopting the acceleration techniques, the FastIPPM shows a 2x-3x speed-up. In addition, positioning accuracy and convergence stability in sparse networks have also been greatly enhanced.

Orthogonal Frequency Division Multiplexing (OFDM) modulation has been used in Starlink, OneWeb and considered for using in many emerging low earth orbit (LEO) satellite communication systems. The explosive growth of OFDM satellite signals provides a plethora of satellite signals of opportunity (SOOPs) for intelligent vehicle systems positioning. In this work, we extend the differential positioning in GNSS to generalized differential positioning with OFDM satellite SOOPs, and propose a bandwidth-efficient framework to estimate the time difference of arrival (TDOA) at the reference node and the rover node. In this framework, the reference node estimates its own time of arrival (TOA) and shares partial subcarrier and symbol information with the rover node to help it achieve a better TOA estimation when its received signal is not strong enough, and the TDOA is obtained via subtraction. Then, a TOA estimation algorithm using only partial subcarriers and symbols of OFDM signals is proposed and the Cramer-Rao lower bound (CRLB) of TOA estimation with parital data is derived. Subsequently, a data compression algorithm is proposed to minimize the transmission bits under the expected TDOA estimation accuracy and rover node's signal-to-noise ratio (SNR). Simulations with Starlink downlink signals and 5 G non-terrestrial network (NTN) downlink New Radio (NR) singals are conducted. The results show that the TOA estimation performance approaches the CRLB and a 10 picosecond-level TDOA performance is achievable with merely using 13$\%$ bits of the Starlink signal or using 34$\%$ bits of the 5G NR signal, which will bring about centimeter-level positioning if enough quantity of TDOAs are available.

To maximize the usage of limited transmission power and wireless spectrum, more communication satellites are adopting precise space–ground beam-forming, which poses a rigorous positioning and timing requirement of the satellite. To fulfill this requirement, a space-borne global navigation satellite system (GNSS) timing receiver with a disciplined high-performance clock is preferable. The space-borne GNSS timing receiver moves with the satellite, in contrast to its stationary counterpart on ground, making it tricky in its positioning algorithm design. Despite abundant existing positioning algorithms, there is a lack of dedicated work that systematically describes the delicate aspects of a space-borne GNSS timing receiver. Based on the experimental work of the LING QIAO (NORAD ID:40136) communication satellite’s GNSS receiver, we propose a fine-tuned positioning algorithm for space-borne GNSS timing receivers. Specifically, the proposed algorithm includes: (1) a filtering architecture that separates the estimation of satellite position and velocity from other unknowns, which allows for a first estimation of satellite position and velocity incorporating any variation of orbit dynamics; (2) a two-threshold robust cubature Kalman filter to counteract the adverse influence of measurement outliers on positioning quality; (3) Reynolds averaging inspired clock and frequency error estimation. Hardware emulation test results show that the proposed algorithm has a performance with a 3D positioning RMS error of 1.2 m, 3D velocity RMS error of 0.02 m/s and a pulse per second (PPS) RMS error of 11.8ns. Simulations with MATLAB show that it can effectively detect and dispose outliers, and further on outperforms other algorithms in comparison.