The prediction of Satellites’ Clock Bias (SCB) plays an important role in optimizing the clock bias parameters in navigation messages, meeting the requirements of real-time dynamic precise point positioning and providing the prior information required for satellite autonomous navigation. Satellite-borne atomic clocks are often affected by many factors in space, which makes it difficult to describe the clocks’ bias and behavior with fixed model to achieve reliable high-precision prediction. The composition and characteristics of clock bias for satellite-borne atomic clock are described and analyzed, a clock bias prediction algorithm based on Nonlinear autoregressive model with exogenous input (NARX) recurrent neural network is proposed, the advantages of this model in SCB and other time series prediction are introduced in detail. The SCB data from four different clock types are selected for calculation and analysis. The comparative results show that, for both 6h and 24h forecasts, the accuracy and stability of NARX model are significantly better than three commonly used models, especially in the prediction of satellite cesium atomic clock.

With the rapid advancement of unmanned system technologies, their applications in transportation, scientific research, economy, resource exploration, and military fields have become increasingly widespread. The navigation system, as a fundamental component of unmanned systems, plays a crucial role in ensuring their stability and reliability. However, as technology evolves, interference targeting Global Navigation Satellite Systems (GNSSs) has escalated, posing significant challenges in the research of unmanned systems. Navigation interference not only disrupts the normal operation of unmanned systems but also emerges as a pivotal element in counter-unmanned system strategies. This paper provides a comprehensive review of the classification of GNSS navigation interference and its potential impacts, thoroughly analyzing and comparing the strengths and weaknesses of various anti-GNSS interference technologies. Finally, the paper offers insights into the future development trends of anti-interference technologies for unmanned systems, aiming to provide valuable references for future research.

To solve the problem of dynamic measurement of deflection of the vertical (DOV) based on the combination of the strapdown inertial navigation system (SINS) and the global navigation satellite system (GNSS), a DOV recovery method based on attitude difference compensation is proposed. The method does not need to model the unknown DOV and avoids the modeling inaccuracy error. Firstly, the attitude angles are estimated by the SINS/GNSS integrated navigation system using Kalman filter. At the same time, the attitude angles are also calculated by integrating the gyro unit of SINS with GNSS. The difference between attitude angles obtained by the two methods is made, and the horizontal attitude difference includes the DOV information and the gyro attitude error with definite characteristics. Then the DOVs in the inertial navigation system are compensated by the horizontal attitude difference with modeled gyro attitude error. The fading memory Kalman filter is used to estimate the attitude angles of INS after DOV compensation, and the attitude angles are taken as the benchmark to make a difference with the attitude angles obtained by SINS/GNSS integrated navigation system. The difference result is the DOVs along the trajectory. In order to find out the optimal fading factor, a traversal search method based on the velocity error of pure inertial solution after DOV compensation is proposed. The shipboard experiment shows that the method can recover the DOVs along the trajectory. After compensation with the recovered DOVs, the positioning error of 21.6-hours pure inertial solution of the SINS is not more than 150 meters, and the positioning accuracy is improved about 6.7 times compared with the Sandwell model compensation.

Based on the application background of magnetic directional navigation on ground-unmanned platforms. The motion of the ground carrier is limited to the two-dimensional plane, which causes the observation matrix composed of magnetic data to be seriously ill conditioned, and it is difficult for traditional methods to estimate stable compensation parameters. In this paper, a regularization method is introduced and combined with Truncated Total Least Squares (TTLS) method and Tikhonov regularization to solve ill-conditioned problems. We control the stability of the solution by introducing regularization variables. During the test, the carrier did not need to travel a specific trajectory (such as an "8" shape) and did not rely on attitude assistance equipment. We verified this method through ground-mounted tests. Test results show that in the case of strong external magnetic interference, this method can solve the ill-conditioned problem of the magnetometer observation matrix by using relatively simple operations, and effectively improve the compensation accuracy of carrier interference magnetic field. The RMSE of the compensated magnetic heading error is better than 0.9°, which is approximately 75% higher than that of the PSO. This method lays the foundation for magnetic field assisted inertial navigation.

Current research on the interference of GNSS (Global Navigation Satellite System) array antennas focuses on the single interference effect and the improvement of interference hardware capability, while the multi-degree-of-freedom (DOF) interference model and mechanism remain to be fully studied. Aiming at this problem, this paper analyzes the preconditions for the definition of anti-jamming degrees of freedom and the characteristics of super-DOF interference through formula derivation and simulation. First, by analyzing the influence of the number of interfering signals on the angular resolution, the prerequisite of the definition of anti-interference degrees of freedom in the airspace is proposed. Second, the definition of anti-interference degrees of freedom is used to calculate the change rule of the critical power of the interference under different numbers of interfering signals. Finally, the influence of super-DOF interference on the array antenna is analyzed. The results show that the prerequisite for the anti-interference freedom of the array antenna is that the distribution interval of the interfering signal is greater than 15°, taking a four-array element uniform circular array antenna as an example. The critical interference power of the array antenna decreases by about 15 dB when the number of interfering signals exceeds the degrees of freedom of the array antenna’s interference immunity, provided that the interference resolution is satisfied. The conclusions of this paper give the critical power change rule of multi-DOF interference and the effect of super-DOF interference, as well as the prerequisites for the setting of interference signals, which can be used, for example, in the deployment of distributed interference sources and the development of anti-jamming algorithms.

To reduce costs, an unmanned swarm usually consists of nodes with high-accuracy navigation sensors (HAN) and nodes with low-accuracy navigation sensors (LAN). Transmitting and fusing the navigation information obtained by HANs enables LANs to improve their positioning accuracy, which in general is called cooperative navigation (CN). In this method, the accuracy of relative observation between platforms in the swarm have dramatic effects on the positioning results. In the popular research, constructing constraints in three-dimensional (3D) frame could only optimize the position and velocity of LANs but neglected the attitude estimation so LANs cannot maintain a high attitude accuracy when utilizing navigation information obtained by sensors installed during maneuvers over long periods. Considering the performance of the inertial measurement unit (IMU) and other common sensors, this paper advances a new method to estimate the attitude of LANs in a swarm. Because the small unmanned nodes are strictly limited by relevant practical engineering problems such as size, weight and power, the method proposed could compensate for the attitude error caused by strapdown gyroscopic drift, which only use visual vectors built by the targets detected by cameras with the function of range finding. In our method, the coordinates of targets are mainly given by the You Only Look Once (YOLO) algorithm, then the visual vectors are built by connecting the targets in the covisibility graph of the nodes in the swarm. The attitude transformation matrices between each camera frame are calculated using the multivector attitude determination algorithm. Finally, we design an information filter (IF) to determine the attitude of LANs based on the observation of HANs. Considering the problem of positioning reference, the field test was conducted in the open air and we chose to use two-wheeled robots and one UAV to carry out the experiment. The results show that the relative attitude error between nodes is less than 4 degrees using the visual vector. After filtering, the attitude divergence of LANs’ installed low precision IMU can be effectively constrained, and the high-precision attitude estimation in an unmanned CN swarm can be realized.