Satellite Internet of Things (IoT) is a promising way to provide seamless coverage to a massive number of devices all over the world, especially in remote

Recently, low-earth orbit (LEO) satellite internet of things (IoT) has received considerable interests due to its global coverage for massive IoT devices distributed over a large area, especially in remote areas, e.g., ocean, desert and forest. Considering relatively long transmission distance between IoT devices and LEO satellite, we propose a low latency and small overhead sourced grant-free random access framework, where active devices send their data signals directly without the grant of LEO satellite. In order to detect active device and recover the corresponding data, we design a joint device and data detection algorithm for massive grant-free random access in LEO satellite IoT. In particular, the active device maps the data to a codeword of a predetermined and unique codebook, and then sends it to the LEO satellite. By detecting the codeword via maximizing the likelihood function of the received signal, the LEO satellite obtains the active device and recovers the corresponding data. Theoretical analysis shows that the proposed algorithm has a fast convergence behavior and low computational complexity. Finally, we provide extensive simulation results to confirm the effectiveness of the proposed algorithm over baseline ones in LEO satellite IoT.

The inter-satellite relative navigation method—based on radio frequency (RF) range and angle measurements—offers good autonomy and high precision, and has been successfully applied to two-satellite formation missions. However, two main challenges occur when this method is applied to multi-microsatellite formations: (i) the implementation difficulty of the inter-satellite RF angle measurement increases significantly as the number of satellites increases; and (ii) there is no high-precision, scalable RF measurement scheme or corresponding multi-satellite relative navigation algorithm that supports multi-satellite formations. Thus, a novel multi-satellite relative navigation scheme based on inter-satellite RF range and angle measurements is proposed. The measurement layer requires only a small number of chief satellites, and a novel distributed multi-satellite range measurement scheme is adopted to meet the scalability requirement. An inter-satellite relative navigation algorithm for multi-satellite formations is also proposed. This algorithm achieves high-precision relative navigation by fusing the algorithm and measurement layers. Simulation results show that the proposed scheme requires only three chief satellites to perform inter-satellite angle measurements. Moreover, with the typical inter-satellite measurement accuracy and an inter-satellite distance of around 1 km, the proposed scheme achieves a multi-satellite relative navigation accuracy of ~30 cm, which is about the same as the relative navigation accuracy of two-satellite formations. Furthermore, decreasing the number of chief satellites only slightly degrades accuracy, thereby significantly reducing the implementation difficulty of multi-satellite RF angle measurements.

The rapid development of multi-satellite formations requires inter-satellite radio frequency (RF) measurement to be both precise and scalable. The navigation estimation of multi-satellite formations using a unified time reference demands the simultaneous RF measurement of the inter-satellite range and time difference. However, high-precision inter-satellite RF ranging and time difference measurements are investigated separately in existing studies. Different from the conventional two-way ranging (TWR) method, which is limited by its reliance on a high-performance atomic clock and navigation ephemeris, asymmetric double-sided two-way ranging (ADS-TWR)-based inter-satellite measurement schemes can eliminate such reliance while ensuring measurement precision and scalability. However, ADS-TWR was originally proposed for ranging-only applications. In this study, by fully exploiting the time-division non-coherent measurement characteristic of ADS-TWR, a joint RF measurement method is proposed to obtain the inter-satellite range and time difference simultaneously. Moreover, a multi-satellite clock synchronization scheme is proposed based on the joint measurement method. The experimental results show that when inter-satellite ranges are hundreds of kilometers, the joint measurement system has a centimeter-level accuracy for ranging and a hundred-picosecond-level accuracy for time difference measurement, and the maximum clock synchronization error was only about 1 ns.

Gold codes are widely used for pseudo‐noise (PN) ranging, and non‐commensurate sampling is an effective solution to improving the tracking performance of digital code tracking loops. However, the sequence transition density of gold codes is low, which limits the achievable ranging accuracy. Here, the authors present a non‐commensurate sampling and double‐loop‐based regenerative PN code tracking approach. With this approach, the tracking of the original regenerative code is equivalent to that of its clock component, and the sequence transition density is maximised. Thus, the tracking error is apparently reduced and optimal tracking performance is achieved. In the double‐loop structure, a unified tracking loop is employed to facilitate performance comparison. Simulation results demonstrate the performance improvement of the proposed approach over the traditional gold code tracking scheme, and the relationship between the non‐commensurate sampling parameters and the achievable tracking accuracy. The approach is especially suitable for precision ranging applications, and can be extended to other applications involving accurate signal tracking in digital equipment.

Low-performing GPS receivers, often used in challenging scenarios such as attitude maneuver and attitude rotation, are frequently encountered for micro–nano satellites. To address these challenges, this paper proposes a modified robust adaptive hierarchical filtering algorithm (named IARKF). This algorithm leverages robust adaptive filtering to dynamically adjust the distribution of innovation vectors and employs a fading memory weighted method to estimate measurement noise in real time, thereby enhancing the filter’s adaptability to dynamic environments. A segmented adaptive filtering strategy is introduced, allowing for flexible parameter adjustment in different dynamic scenarios. A micro–nano satellite equipped with a miniaturized dual-frequency GPS receiver is employed to demonstrate precise orbit determination capabilities. On-orbit GPS data from the satellite, collected in two specific scenarios—slow rotation and Earth-pointing stabilization—are analyzed to evaluate the proposed algorithm’s ability to cope with weak GPS signals and satellite attitude instability as well as to assess the achievable orbit determination accuracy. The results show that, compared to traditional Extended Kalman Filters (EKF) and other improved filtering algorithms, the IARKF performs better in reducing post-fit residuals and improving orbit prediction accuracy, demonstrating its superior robustness. The three-axes orbit determination internal consistency precision can reach the millimeter level. This work explores a feasible approach for achieving high-performance orbit determination in micro–nano satellites.