An improved adaptive Huber filter algorithm is presented to model error and measurement noise uncertainty. The adaptive algorithm for model error is obtained using an upper bound for the state prediction covariance matrix. The measurement noise is estimated at each filter step by minimizing a criterion function which was original from adaptive Huber filter. A recursive algorithm is provided for solving the criterion function. The proposed adaptive filter algorithm was successfully implemented in relative navigation using global position system for spacecraft formation flying in high earth orbits with real orbit perturbations and non-Gaussian random measurement error. Simulation results indicated that the proposed adaptive filter performed better in robustness and accuracy compared with previous adaptive algorithms.

It's important for China's Land Observing Satellite-1 to run in a repeat ground-track orbit with almost constant altitude over the same geographical area, so the satellite can have a high-precision deformation monitoring capability. The strong continuity of reference trajectory and high computational efficiency of the algorithm are required in the satellite orbit design stage. To fulfill those requirements, we first propose a fast orbit design method based on objective dimensionality reduction and decoupling optimization, which makes the satellites drift naturally without additional propellant consumption. Besides, a completely different convergence standard for the orbit design is introduced, which greatly reduces the complexity of the optimization process by decreasing the optimization objective dimension. Meanwhile, an optimal strategy keeping the argument of latitude constant is proposed, which reduces the coupling characteristics among optimization objectives. In addition, the optimization model of the repeat ground-track orbit is established. Then the steepest descent method is introduced to solve this optimization problem. Simulation results in the high-order gravity force reveal that the position accuracy and velocity accuracy of the reference trajectory are up to millimeters and centimeters per second respectively. The calculation efficiency of our method is about 56 times faster than that of the NSGA-II method.

Onboard GNSS-based relative baseline determination has emerged as a primary solution for formation-flying satellites dedicated to mapping and remote sensing missions. For ambiguity resolution (AR), the double-difference (DD) method is widely adopted in relative baseline determination. However, this method entails relatively complex satellite pairing, which not only increases computational load and complicates the processing workflow but also imposes higher requirements on onboard embedded computing and storage resources, thereby introducing potential risks to engineering implementation. To address these issues, this paper proposes incremental refinements to the single-difference (SD) model by introducing the combined GPS/BDS uncombined SD method for closely spaced formation satellites. By leveraging the enhanced satellite visibility of the combined GPS/BDS constellation and adopting a purely geometric approach, high-precision real-time relative baseline determination results are achieved. Validation using onboard observation data from the Lutan-1 satellite mission of China demonstrates that centimeter-level relative baseline determination accuracy can be attained.

Nearly 70% casing damaged well in complicated fault oil and gas field were caused by geology and engineering. These two factors cause the casing plastically imbalanced by ununiform load that applies on, thus the imbalanced section where stress is concentrated makes casing bending, deformation or breaking. Metal magnetic memory technique was an effective method to find stress concentration zone. For nonstationary leakage magnetic signals contained noise, the threshold value is determined based on 3sigma-rule applied electronic measurement, wavelet denoising is introduced in test systems of metal magnetic memory. As a result, can reserve the available signal and reduce the noise, character of magnetic signals (gradate) right is obtained

In challenging environments, there often exist problems of false alarms and missed detections in real-time kinematic (RTK) ambiguity resolution, which significantly reduce the reliability and availability of position information. To address these issues, a machine-learning method is proposed to conduct a correctness check on RTK ambiguity fixing, aiming to reduce the occurrences of false alarms and missed detections. The inter-system differential RTK model is adopted. Compared with the traditional RTK model, this model can provide an effective feature, namely the differential inter-system biases (DISB), to improve the accuracy of machine-learning classification. This is because when the RTK ambiguity is correctly fixed, the DISB usually appears as a stable constant. In addition to DISB, features that are strongly related to ambiguity fixing, such as the ratio value, DOP value, and residuals, are also comprehensively utilized. This method is verified by an open-source, large-scale, and diverse GNSS/SINS dataset—SmartPNT-POS. The experimental results show that, compared with the traditional method of relying solely on the empirical ratio value for ambiguity fixing verification, the missed detection probability of this method is reduced by 2%, the false-alarm probability is decreased by 29%, and the positioning accuracy is improved by approximately 7%. Moreover, compared with other features, the DISB feature provides the highest contribution rate in the machine-learning classification model.