Zhonghe Jin

In absolute distance measurement and positioning applications, atmospheric refraction error is a critical factor limiting measurement accuracy. Temperature plays a dominant role in computing the atmospheric refractive index. However, accurately acquiring the temperature field along the ranging path in complex and dynamic outdoor environments remains challenging due to limited sensor deployment and environmental nonstationarity. We propose a spatiotemporal temperature data fusion method for atmospheric refraction correction, which integrates the strengths of the generalized regression neural network (GRNN) and Kriging interpolation within a Kalman filter. This method achieves dynamic prediction and high-accuracy reconstruction of temperature parameters. The proposed method is systematically validated through simulation analysis as well as indoor and kilometer-scale outdoor experimental measurements. The simulation results demonstrate that Kalman filter expanded fusion (KFEF) outperforms the traditional interpolation method radial basis function (RBF) and the state-of-the-art spatiotemporal interpolation and prediction methods spatiotemporal Kriging (STK) and Gaussian process (GP), in terms of both reconstruction accuracy and stability of the temperature field. Specifically, KFEF achieves a 61.54% reduction in root mean square error (RMSE) compared with RBF and reductions of 34.21% and 32.43% relative to STK and GP, respectively. This indicates its practical value for long-distance high-precision ranging engineering applications. Furthermore, the proposed spatiotemporal data fusion framework is highly general and scalable. It can also be applied to other temperature field prediction and reconstruction problems.

The dual transponder carrier ranging system, an enhancement over the dual one-way ranging system, provides high precision relative distance measurements without imposing stringent time synchronization requirements. However, a critical limitation to its use in absolute distance measurement is the initial value change phenomenon, which manifests as abrupt phase changes after every system restart. Through theoretical analysis, the root causes of this phenomenon are identified as a fractional output-to-input frequency ratio in the frequency synthesizer and a non-integer coherent turnaround ratio. To validate the theoretical analysis, simulations are conducted using Simulink, and experimental verification is performed on an S-band and C-band hardware platform. Both simulation and experimental results demonstrate that configuring both ratios as integers effectively suppresses these abrupt phase changes. These findings establish an integer-ratio design rule that is independent of specific frequency plans, providing a general design guideline for coherent transponder ranging systems intended for absolute distance measurement, as well as a reference for frequency planning in other absolute distance measurement systems of the same kind.

With the rapid development of the Industrial Internet of Things (IIoT), the application scale of Time-Triggered Ethernet (TTE) technology in the IIoT has been increasingly expanding. To address the issues of rapidly increasing computation time and deteriorating scheduling quality in traditional scheduling algorithms for large-scale TTE applications, this paper proposes a hybrid scheduling algorithm based on critical-link optimization. A large-scale TTE message scheduling model is established based on the characteristics of Time-Triggered (TT) messages, and the constraints of TT scheduling are mathematically abstracted. After identifying the critical link of the network, a time slot balancing scheduling algorithm based on static priority is adopted for the link. The algorithm searches for the optimal scheduling time of current message by time-sliding within the current maximum time gap of TT messages from the center to both sides, maximizing the balance of TT message intervals to reduce the impact on Best-Effort (BE) message transmission performance. An improved genetic algorithm is proposed for the scheduling of the entire network to further enhance the global optimization capability, which takes the scheduling results of the critical link as the genes of initial population. The TT scheduling constraints are converted into the fitness function and the optimized genetic operators are developed for the genetic algorithm. Simulation results showed that the proposed algorithm can significantly reduce computing time and increase the success rate of message scheduling. At the same time, the scheduling results exhibit a better degree of TT message balance and can effectively reduce the transmission delay and jitter of BE messages as message load increases compared with traditional algorithms, making it better meet the scheduling requirements of large-scale TTE application scenarios.

Under dynamic conditions, the smearing effect of star spots on the image plane reduces centroid extraction accuracy, which has an impact on attitude estima

Reliable inter-satellite measurements play a vital role in enabling autonomous positioning, navigation, and time synchronization in satellite formations. However, one of the major challenges affecting measurement accuracy is equipment delay. While existing calibration methods offer partial solutions, many are either restricted to frequency division multiple access systems, which are less scalable for growing satellite constellations, or rely on extra hardware that increases power consumption, posing a serious limitation for microsatellites. In this study, we introduce a lightweight and effective calibration approach specifically designed for time division multiple access-based microsatellite systems. This method avoids the need for additional calibration devices, helping maintain energy efficiency without compromising accuracy. Both theoretical modeling and experimental validation were conducted to assess the method’s performance. Results show that our approach achieves millimeter-level precision using a standard miniaturized frequency source. Specifically, calibration brings the measurement deviations down to less than 7 mm for distance and under 4.558 ns for time difference, which surpasses the accuracy of existing techniques. The proposed solution not only meets the precision demands of modern satellite formations but also supports more scalable and energy-aware deployment of microsatellite networks.

With the extended operational time of micro-satellites in orbit, the statistical characteristics of process and measurement noise often deviate from their nominal values, leading to a decline in the performance of traditional extended Kalman filters (EKFs) for attitude estimation and gyro drift compensation. The adaptive EKF algorithm based on variational Bayesian theory requires the nominal process noise matrix to closely match the true values; otherwise, its accuracy is significantly compromised. To address this challenge, this paper proposes an enhanced adaptive multiplicative EKF (AMEKF) algorithm based on variational methods. Compared to the traditional AMEKF, the proposed algorithm processes the gyroscope’s measured angular velocity using total variation, effectively removing the two zero-mean Gaussian noises that correspond to the process noise in this context. By using the processed angular velocity for attitude and gyro drift estimation, the algorithm bypasses the uncertain process noise, resulting in improved accuracy compared to traditional algorithms. Simulation results demonstrate that, in comparison with the traditional AMEKF, the proposed algorithm reduces gyro drift estimation error by approximately 99% and attitude estimation error by about 72%–93%. The simulation results of typical on-orbit operation modes for microsatellites show that, although the attitude estimation accuracy decreases under more complex angular velocity conditions, the three-axis attitude error angles can still reach [ 1.31   1.37   7.94 ] T arcsec. The results further confirm that the proposed algorithm can effectively handle uncertain process and measurement noise, achieving high precision in both attitude and gyro drift estimation.

This paper presents a high-performance MEMS accelerometer that utilizes a single set of multi-functional gap-varying double-sided parallel plate (DSPP) capacitors. We propose a self-centering method for the geometric center of the DSPP. The self-centering method enables a linear relationship between the force-to-rebalance (FTR) output and the applied acceleration, effectively addressing the inherent nonlinearity issue of three-electrode accelerometers. Within the range of −1 g to 1 g (g = 9.8 m s⁻²), the accelerometer achieves a nonlinear error of 106 ppm. A novel FTR control method based on three-electrode configuration and Pythagorean identity is proposed to realize a reduced constant-stiffness and high-linearity FTR. It enables a high-degree integration of displacement detection, FTR, stiffness tuning, and position calibration on a simple three-electrode DSPP structure. Experimental results demonstrate the effectiveness of self-centering and constant quasi-zero stiffness control, resulting in a bias instability of about 0.57 μg and a noise floor of 0.85 μg/√Hz for the proposed accelerometers. In comparison to the conventional accelerometer with multiple capacitors, the proposed accelerometer achieves a full-scale range twice as wide as the first (up to ±4.22 g) and about 50% reduction of layout area of capacitors with little accuracy degradation.

The dynamic performance of star sensors is primarily affected by the smearing effect of star spots. To enhance the dynamic performance, it is necessary to perform blur kernel estimation and restoration of star images. However, the accuracy of motion parameters estimation of existing algorithms is not high enough, which limits the restoration effect. An efficient motion-blurred star image’s motion parameters estimation algorithm based on Zernike moments is presented in this study to upgrade the accuracy of centroid extraction and attitude estimation under dynamic conditions. The proposed algorithm improves the dynamic performance of star sensors through two aspects. First, a unitized roof model is established for the dynamic star spots. Second, the Zernike moments of the unitized roof model are derived, and the subpixel edge detection method is used to estimate the angle and length of the star streak for obtaining the motion parameters of the star image. The effectiveness and excellent performance of the proposed algorithm are verified using simulations and ground experiments. Experiments reveal that this algorithm is robust to noise, increases the estimation accuracy of blur parameters, and thus improves the accuracy of star spot extraction. Meanwhile, the algorithm has less time consumption, and is suitable for boosting the dynamic performance of star sensors.