1082 results about "Satellite system" patented technology

The disclosed technology is generally directed to a method for controlling a satellite system comprising at least one satellite. The method may include receiving and processing a set of control parameters associated with an orientation of the at least one satellite via a classic control model to generate a set of actions to control the orientation of the at least one satellite and storing the set of actions as data in a buffer. The processing and storing are iteratively repeated until the data stored in the buffer exceeds a threshold. When the data stored in the buffer exceeds the threshold, the method may further include iteratively implementing, based on each of the set of control parameters and the data stored in the buffer, the machine learning model to control the orientation of the at least one satellite to stabilize the at least one satellite.

A method for retrieving tropospheric wet delay and atmospheric water vapor content over polar sea ice with TDS-1 satellite grazing angle spaceborne GNSS-R is provided, including: Si, obtaining TDS-1 GNSS-R raw intermediate frequency signal data, VMF3 grid data, GPT3 grid data and ERA5 data; S2, correcting an error of tropospheric wet delay estimation of grazing angle spaceborne GNSS-R; S3, constructing a grazing angle spaceborne GNSS-R tropospheric wet delay estimation model; S4, calculating grazing angle spaceborne GNSS-R ZWD; S5, calculating a Tm value of a target point based on GPT3 model, substituting the Tm value into a conversion factor II, and combining calculated GNSS-R ZWD to obtain a GNSS-R IWV estimated value; and S6, verifying inversion performance of GNSS-R ZWD and integrated water vapor (IWV) by using reference data.

The invention discloses a low-orbit satellite beam resource allocation method based on deep reinforcement learning, and the method comprises the steps: firstly building a satellite system model according to a low-orbit satellite system hopping beam scene, obtaining the communication traffic demand and geographic position of a ground user through a satellite traffic monitoring facility, and carrying out the preprocessing; secondly, carrying out user clustering on information of communication traffic demands of ground users, and dividing a ground cell into a hot spot area and a non-hot spot area; and then based on a satellite system model, establishing an optimization problem of multi-beam resource allocation and control, adopting a deep reinforcement learning method, dynamically adjusting a beam resource allocation strategy, allocating narrow beam resources to a hot spot area, and allocating wide beam resources to a non-hot spot area to realize system throughput maximization and user fairness. According to the method, the resource allocation problem in the low-orbit satellite hopping beam scene is effectively solved, and the throughput of a satellite system is improved while the user fairness is ensured.

Provided is a detection method of spaceborne GNSS-R original intermediate frequency coherent reflection signals in ocean, polar and inland water areas, including: acquiring spaceborne GNSS-R original intermediate frequency signal data of TDS-1 or CYGNSS and preprocessing the data; selecting coherent detection feature engineering; setting data labels of different scenes and coherent and incoherent reflected signals; dividing a training set and a test set; and training and testing a multimode-oriented hybrid model for coherent and incoherent detection and classification of spaceborne GNSS-R signals, using the training set to train a model, applying a trained detection model to a test data set, and comparing and evaluating obtained detection results with a classical coherent detection algorithm.

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) and GNSS (Global Navigation Satellite System) robust adaptive fusion method for high-precision three-dimensional surface deformation monitoring, which belongs to the technical field of space geodetic survey and remote sensing, and comprises the following steps of: constructing a time sequence deformation field based on a small baseline set time sequence InSAR processing technology, and resampling GNSS observation data to a grid consistent with the InSAR by using Kriging spatial interpolation; a Helmert variance component estimation method is adopted to adaptively estimate variance components of InSAR and GNSS data, and reasonable weight fixing of a multi-source observation value is achieved; iterative reweighted least square estimation is introduced, outlier influences in various observation data are dynamically restrained through a Tukey double-weight function, robust optimization of a fusion model is achieved, and therefore a high-precision three-dimensional deformation field is reconstructed. The method comprises the steps of Helmert weight fixing, IRLS robust, variance component updating and a closed-loop feedback mechanism of weight matrix optimization.

The invention discloses a PPP-RTK time delay processing method and device in conversion from SSR to OSR, and relates to the technical field of global navigation satellite system positioning, and the method comprises the steps: converting orbit, clock error, ionosphere and troposphere delay SSR parameters provided by a server into OSR virtual observation values through geometric distance calculation and projection function mapping; based on a constant velocity model and a static random process, predicting a correction amount of a future moment, and correcting an OSR virtual observation value through state transition matrix compensation time delay; combining server side parameters and user side observation data, recursively generating an error covariance matrix, dynamically adjusting a state prediction weight, and obtaining covariance information through adaptive filtering optimization positioning calculation; and realizing ambiguity rapid fixation and centimeter-level positioning by using the corrected OSR observation value and covariance information. According to the method, real-time centimeter-level positioning in a high-delay scene is realized through a dynamic correction value prediction method and a recursive covariance estimation method.

The invention discloses a high-precision train locomotive positioning method and system based on a GNSS (Global Navigation Satellite System) and INS (Inertial Navigation Satellite System) loose combination mode, and the method realizes the precise positioning of a train by combining the data of an IMU (Inertial Measurement Unit) and a GNSS (Global Navigation Satellite System). The method comprises the following steps: firstly, acquiring IMU data, performing error correction, and outputting corrected acceleration and angular velocity; meanwhile, a GNSS signal is obtained and a pseudo-range measurement value is extracted. Then, inertial navigation calculation is carried out, including attitude calculation, position and speed calculation and sensor deviation updating; through Kalman filtering fusion, state estimation and error correction are carried out by using complementary characteristics of the INS and the GNSS, so that the positioning precision is improved. In the initialization stage, the normal work of the system is ensured through the installation and parameter setting of the IMU and the GNSS. Finally, through Kalman filtering processing, accurate train position, speed, attitude and sensor deviation data are output, and reliable data support is provided for a train control system.

Novel tools and techniques are provided for implementing detection and estimation of direct and reflected navigation satellite (e.g., global navigation satellite system (“GNSS”), etc.) signal parameters in a multipath environment. In various embodiments, logic of semiconductor package that is disposed on a user device concurrently receives a plurality of signals from a satellite(s), each signal travelling along a different path between each satellite(s) and the user device within a multipath environment. The logic identifies two or more signal peaks that fall within a tracking aperture based on analysis of the received signals, and determines peak parameter estimates for each signal peak based on measurements of signal parameters from at least one signal peak. The logic provides the determined peak parameter estimates for each signal peak to a position engine (“PE”) of the user device to calculate a navigation solution (e.g., position, velocity, and / or time, etc.) for the user device.

The invention relates to the technical field of meteorological remote sensing, in particular to a self-adaptive partition water vapor chromatography method and system based on multi-scale Bayesian prior, and the method comprises the steps: S1, obtaining and processing multi-source observation data of a to-be-analyzed region; s2, constructing a multi-scale grid of the tomographic inversion region; s3, multi-scale representation of the water vapor density is established, and a Bayesian prior constraint model is constructed; s4, constructing a three-dimensional chromatography inversion equation set; and S5, carrying out Bayesian inversion solution. According to the method, global navigation satellite system observation data, meteorological station data and sounding station data are fused, Bayesian prior constraints are constructed, and a multi-scale representation and self-adaptive partitioning method is adopted, so that the number of parameters is effectively reduced, and the stability, precision and calculation efficiency of tomography inversion are improved.

The invention discloses a GNSS (Global Navigation Satellite System) / inertial navigation tightly integrated navigation method and system based on robust auto-velocity constraint factor graph optimization, and the method specifically comprises the following steps: extracting pseudo-range and Doppler observation data from original observation information of a GNSS satellite, and obtaining accelerometer and gyroscope data from an INS (Inertial Navigation System); constructing a pseudo-range residual block, a Doppler residual block, an inertial navigation pre-integration residual block and a speed constraint factor, and integrating into a sliding window estimator based on factor graph optimization; according to the size of a set sliding window, adding all residual blocks in the window to form an optimized objective function; performing a first round of factor graph optimization to obtain a preliminary state estimation result, and endowing the observed quantity containing gross error with a relatively low weight; and updating the prior weight matrix, and carrying out second round of optimization to obtain a joint optimal estimation result of a plurality of moment states in the current time window. According to the method, the influence of gross error-containing observation information on weight estimation and state estimation is suppressed, the positioning precision is high, the robustness is strong, and the reliability is high.

In estimating a position of a vehicle utilizing a global navigation satellite system (GNSS), it is desirable to exclude outliner GNSS measurements due to navigation signals via non-light-of-sight paths from the GNSS to the vehicle, but it leads to a distorted satellite geometry distribution. Complementariness between low-lying visual landmarks and healthy but high-elevation satellite measurements is explored to improve the geometry constraint. Measurements of an inertial measurement unit, low-lying visual landmarks captured by a forward-looking camera onboard the vehicle, and healthy but high-elevation satellite measurements are tightly-coupled integrated via sliding window optimization of system states used in a factor graph. To improve estimation performance, good initial guesses of system states are important. As such, initial guesses of velocity set and position set inside a sliding window are estimated simultaneously based on data of Doppler measurement, double-differenced (DD) pseudorange measurement and DD carrier-phase measurement as obtained in GNSS measurements.

To eliminate the influence of station position fluctuations in a satellite navigation system. [Solution] In satellite navigation systems, including the US GPS and Japan's Quasi-Zenith Satellite System, when multiple reference stations are installed to form a wide-area differential correction system (WADGPS), previously, position errors caused by fluctuations in station position would remain in the position information calculated by the user receiver.However, by adding a correction amount to the correction information provided by WADGPS in advance to cancel this, the position errors caused by fluctuations in station position at the user station can be reduced without changing the calculation processing content at the WADGPS user station, thereby improving the accuracy of the position information calculated by the user station.

The invention discloses a combined navigation method and system of an unmanned surface vehicle. The method comprises the following steps: forming a GNSS (Global Navigation Satellite System) / INS (Inertial Navigation System) integrated navigation system by using a GNSS (Global Navigation Satellite System) and a strapdown inertial navigation system (INS), and establishing a mathematical model of a GNSS / INS integrated navigation system filter; the mathematical model comprises a state equation and a measurement equation; establishing a mathematical model of the influence of the GNSS auxiliary information on the GNSS positioning precision so as to preliminarily adjust the measurement noise in the measurement equation; through an improved Sage-Husa adaptive filtering algorithm, adaptive estimation is carried out on the preliminarily adjusted measurement noise, and a measurement noise estimation final value is obtained; and according to the motion characteristics of the unmanned surface vehicle, adding motion constraint conditions to the measurement equation. The GNSS auxiliary information mathematical model based on multivariate function fitting is established, the influence of the external environment on the positioning precision is objectively reflected, the measurement noise in the filter is dynamically adjusted, and the positioning precision of the integrated navigation system under the interference condition is remarkably improved.

The invention relates to the technical field of signal analysis, in particular to a GNSS (Global Navigation Satellite System) detection method and system based on machine learning, and the method comprises the steps: receiving a navigation radio-frequency signal containing reality and deception, and carrying out the down-conversion of an RF (Radio Frequency) front end to obtain intermediate-frequency data; gNSS signal capture is completed by searching correlation peaks in frequency and code phases based on intermediate frequency data; in the capturing stage, a motion state, a correlation peak number, a noise value, capturing parameters and peak + / -1 chip sampling features are extracted and input into a machine learning classification model to judge whether cheating exists or not; if so, separating the deception signal from the real signal, and respectively sending the deception signal and the real signal into independent tracking channels; measuring an array element carrier relative phase by an array antenna, establishing a phase center model, and correcting to obtain an effective phase center and a differential baseline; according to the method, a deception azimuth angle and a pitch angle are obtained, a beam weight is calculated, a deception direction is subjected to null setting, a real direction is subjected to main lobe protection, and a weight value is fed back in real time to improve a signal carrier-to-noise ratio and locking stability. According to the invention, the problem that the existing commercial GNSS signal is low in power and easy to be interfered by deception is solved.

The invention discloses a road risk grading early warning method and system based on a Beidou satellite system, and the method comprises the steps: firstly carrying out the real-time positioning of a vehicle through Beidou dual-frequency signals, inertial navigation data, road side unit differential data and vehicle-mounted sensor data, and obtaining the precise position information; fusing the real-time position of the vehicle with meteorological data, vehicle-mounted OBD parameters and social media public opinion data to generate a dynamic risk factor matrix; a fuzzy rule base is constructed based on expert experience, the fuzzy weight of each risk factor in a matrix is obtained, meanwhile, the time sequence weight of each factor is predicted by means of an LSTM model, and a final road risk score is obtained through dynamic weighting. And performing graded early warning on the road risk in combination with the driver portrait, and feeding back and updating the fusion parameter, the fuzzy rule base or the LSTM model parameter according to the early warning effect to form a closed-loop optimization mechanism. Dynamic coupling analysis of multi-dimensional risk factors is realized, and the real-time performance and accuracy of road risk early warning are improved.

The invention relates to a modernized global navigation satellite system receiver. GNSS receivers and systems within such receivers use improvements to reduce memory usage while providing sufficient processing resources to directly receive, capture and track E5 band GNSS signals (in one embodiment, not attempting to receive L1 GNSS signals). Other aspects are also described.

The invention relates to a CNN-GRU-MHSA navigation method and a CNN-GRU-MHSA navigation system for improving pseudo measurement noise covariance under GNSS interruption. Comprising the following steps of: combining three-dimensional output of a gyroscope and three-dimensional output of an accelerometer in an IMU (Inertial Measurement Unit) in a GNSS (Global Navigation Satellite System) observation period as well as a three-dimensional speed and a three-dimensional course angle calculated by an INS (Inertial Navigation System) into an input vector, inputting the input vector into a trained CNN-GRU-MHSA (Global Navigation Satellite System) hybrid neural network, and outputting to obtain a pseudo GNSS position increment in the GNSS observation period. And synthesizing the pseudo GNSS position increment and the GNSS position information of the previous epoch to obtain pseudo GNSS position information of the current epoch. And then, adding an accumulated error compensation factor related to time into Sage-husa adaptive filtering to estimate a pseudo measurement noise covariance matrix. And finally, transmitting the pseudo GNSS position information and the pseudo measurement noise covariance matrix into an integrated navigation Kalman filter for resolving so as to correct an INS navigation resolving result.

The invention relates to the technical field of satellite internet, and discloses a low-orbit satellite phased array multi-beam interference modeling and suppression method and system, and the method comprises the steps: selecting a Kaiser window as a core filtering method, and achieving the optimization of beam characteristics through the dynamic adjustment of a shape parameter beta; generating an initial beam directional diagram based on a digital phase matching method, and multiplying the Kaiser window function coefficient by the excitation weight of the 64-array-element linear array element by element to realize spatial domain weighted filtering; the method comprises the following steps: constructing a training data set containing multi-scene interference characteristics, calculating a corresponding covariance matrix and an accurate inverse matrix thereof to form a sample pair, designing a deep neural network architecture, inputting a flattened covariance matrix vector, and learning a complex nonlinear mapping relation from the covariance matrix to the inverse matrix through a multi-layer full-connection structure; a mean square error is used as a loss function to constrain network output precision, and a multi-beam interference system model is constructed; according to the invention, stable and efficient communication of the low-orbit satellite system in a complex electromagnetic environment and under rapid channel change is ensured.

Provided is a method for a user equipment (UE). The method includes detecting a status of a Global Navigation Satellite System (GNSS) position fix for the UE; in accordance with a determination that the GNSS position fix for the UE is invalid, generating, for transmission to a network device, a first GNSS measurement parameter including a first GNSS measurement periodicity and a first GNSS measurement gap; receiving, from the network device, a second GNSS measurement parameter including a second GNSS measurement gap; and performing a GNSS measurement based on the received second GNSS measurement parameter, for acquiring a valid GNSS new position fix, wherein the UE remains in Radio Resource Control (RRC)_Connected mode.

The invention provides a double-antenna attitude and orientation method and system based on integrated navigation, and the method comprises the steps: combining an INS (inertial navigation system) and a double-antenna GNSS (global navigation satellite system), and enabling the system to output continuous and stable high-precision carrier attitude information when a GNSS signal is interfered by using the provided double-antenna GNSS / INS integrated navigation method. According to the method, a course updating strategy is provided, the motion state of the device is detected, course information at the first static moment in the dynamic and static conversion process is stored in time, and then Kalman filtering course updating is conducted through the information. Even if no course information is output subsequently, the system can output high-precision attitude information for a long time. According to the method, the advantages of the double-antenna GNSS and the advantages of the INS are combined, the orientation precision and the real-time performance of the system are improved when the GNSS signal is unavailable, and the method has good practical value.

The invention discloses a geotechnical engineering slope stability real-time monitoring method and a geotechnical engineering slope stability real-time monitoring system, which are characterized in that a blind source separation technology combining independent component analysis and physical constraint is introduced, an original displacement time sequence and an environment temperature time sequence of a plurality of GNSS (Global Navigation Satellite System) measuring points are regarded as multi-channel mixed signals, and statistical independence among signal sources is utilized to monitor the stability of a slope in real time. Periodic environment noise, instrument random noise and drift and real slope deformation signals are effectively separated from the mixed observation signals; and then, through correlation verification with physical quantities such as the field environment temperature and the like, automatic calibration is performed on the separated source signals, and temperature effect source signals and long-term creep source signals with physical labels are accurately identified and extracted, so that accurate elimination of noise components and high-fidelity reconstruction of pure deformation signals are realized. Therefore, the accuracy of real-time monitoring of the slope stability of geotechnical engineering can be effectively improved, and a solid data basis and a decision basis are provided for early warning and prevention of slope disasters.

Embodiments generally relate to small satellites. Example small satellites may include a satellite body having a rectangular tile shape, with a substantially flat front side having a length and width and being separated from a substantially parallel back side by a depth, wherein the depth is equal to or less than a third of the length and the width. A patch antenna array may be positioned on the front side and / or a large patch antenna may be positioned centrally on the front side. A solar panel may be positioned on or coupled to the back side.

The embodiment of the invention provides a downlink signal sending method and a downlink signal processing method, which are characterized in that a downlink signal is obtained on the satellite side based on a supplementary synchronization sequence, and then time-frequency synchronization is carried out on the terminal equipment side by using the downlink signal from the satellite. In the application, the terminal device can realize time-frequency offset tracking compensation without acquiring satellite ephemeris information and using GNSS (Global Navigation Satellite System) equipment, so that the influence of ephemeris errors and GNSS errors on time-frequency synchronization performance after the terminal device is shut down for a long time is eliminated, and the problem that the time-frequency synchronization performance of the terminal device is influenced in the related technology is solved. The problem of low precision of time-frequency offset tracking compensation in the satellite-ground communication process is solved, and the time-frequency synchronization success rate of satellite-ground communication under the conditions of inaccurate ephemeris and high speed is improved.

A positioning method and an electronic device, relating to the technical field of positioning. The method comprises: acquiring reference information of an object to be positioned at at least one moment, wherein the reference information comprises inertial measurement unit (IMU) information, visual information, and global navigation satellite system (GNSS) information (S102); on the basis of the IMU information, performing IMU state propagation to obtain IMU positioning data (S104); on the basis of the GNSS information, performing GNSS state propagation to obtain GNSS positioning data (S106); and by means of a square root inverse sliding window filter (SRI-SWF), positioning said object on the basis of the IMU positioning data, the GNSS positioning data, and the visual information (S108).

The invention discloses a method for accelerating convergence of GNSS (Global Navigation Satellite System) real-time satellite clock error estimation, which belongs to the field of satellite positioning time service and comprises the following steps of: constructing an error equation corresponding to each GNSS satellite of each observation station; combining the error equations respectively corresponding to the plurality of GNSS satellites of the plurality of observation stations to obtain an error equation in a matrix form; according to the GNSS real-time satellite clock error estimation method, the GNSS ultra-fast forecast satellite clock error is introduced, the process of real-time satellite clock error estimation through the Kalman filtering method is restrained, and due to the fact that the precision of the GNSS ultra-fast forecast satellite clock error is high, convergence of GNSS real-time satellite clock error estimation is accelerated, and a high-reliability GNSS real-time satellite clock error product is obtained.

A wireless transmit receive unit (WTRU) is configured to receive configuration information for reporting Global Navigation Satellite System (GNSS) assistance information (AI). The configuration information comprises a reporting trigger threshold that is at least one of a distance threshold or a time offset threshold. The WTRU determines that the reporting trigger threshold is exceeded and reports GNSS AI. The GNSS AI comprises at least one of a GNSS validity duration or a GNSS acquisition time. The GNSS validity duration comprises an indication of a time duration associated with a validity of a GNSS acquisition, an indication of a time duration associated with expiration of the GNSS acquisition, or a WTRU location associated with the GNSS acquisition. The GNSS acquisition time comprises a time to acquire a GNSS position.

The invention relates to the technical field of satellite attitude control, in particular to a satellite-borne laser semi-autonomous pointing control method, electronic equipment and a storage medium. The method is completed by cooperation of a ground system and an on-satellite system, the ground system calculates a top target passing moment T * in advance, generates a unified laser pointing control instruction and injects the instruction into a satellite; and the satellite-borne system receives and analyzes the instruction, obtains parameters such as T * and beam selection, and then autonomously controls a specified load for work preparation. And the satellite-borne system autonomously calculates the optimal moment T1 and the target attitude when passing through the calibration field through high-precision orbit extrapolation based on the latest navigation data, finally autonomously executes attitude maneuver and laser pointing tasks, and autonomously returns after the tasks are completed. Instructions in a uniform format are generated and injected from the ground, ground operation is greatly simplified, efficient cooperation of ground decision triggering and on-satellite autonomous execution is achieved, pointing errors caused by orbit extrapolation are greatly reduced, the pointing precision and the task success rate are improved, and universality is good.

The disclosure relates to a system and a method for performing a Global Navigation Satellite System (GNSS) measurement in Non-terrestrial network (NTN). In some aspects, a user equipment (UE) may comprise: at least one antenna; at least one radio, configured to perform wireless communication using at least one radio access technology; and one or more processor coupled to the at least one radio, wherein the at least one radio and the one or more processor are configured to cause the UE to detect an event to trigger a Global Navigation Satellite System (GNSS) measurement; transmit, to a network device, a request to perform an event-triggered GNSS measurement, in response to detection of the event; receive, from the network device, a configuration for an event-triggered GNSS measurement gap during which the UE performs the event-triggered GNSS measurement in a connected state; and perform the event-triggered GNSS measurement in the connected state during the event-triggered GNSS measurement gap.

The invention relates to the technical field of satellite conduction integration, and discloses a high and low orbit satellite conduction integration method and system, and the method comprises the steps: carrying out the spatial characteristic analysis of a high orbit navigation satellite signal and a low orbit communication satellite signal, and obtaining visible relation data and Doppler frequency shift characteristic data; executing high-orbit and low-orbit satellite conduction switching analysis, and generating a self-adaptive threshold parameter of conduction service switching between the high-orbit and low-orbit satellites; a high-orbit and low-orbit integrated inter-satellite communication network is established between the high-orbit navigation satellite cluster and the low-orbit communication satellite cluster; conducting resource collaborative allocation is executed, and a resource scheduling instruction is generated; according to the method, deep integration of communication and navigation functions is realized, the limitation of function separation of a traditional satellite system is broken through, and the problem of unstable control caused by dynamic change of network topology of the high-orbit and low-orbit satellites is solved.

The invention belongs to the technical field of geological disaster monitoring, and particularly relates to a landslide multi-modal data fusion method based on a dynamic gating mechanism, which comprises the following steps: respectively carrying out feature extraction on remote sensing image data and GNSS (Global Navigation Satellite System) data, and fusing the GNSS data and meteorological data by using an SARIMAX model to obtain fusion features of the GNSS data and the meteorological data; the method comprises the following steps: firstly, carrying out remote sensing image data on a landslide to obtain a fusion feature, then carrying out secondary fusion on the remote sensing image data and the fusion feature to obtain a multi-modal fusion feature, and then monitoring the dynamic change of the landslide in real time by using a GRU-Attention multi-modal landslide monitoring model, thereby automatically identifying the influence of external environmental factors on displacement while monitoring, and improving the comprehensive capability and response speed of monitoring.