1430 results about "Satellite system" patented technology

Disclosed is a three-dimensional deformation field modeling method fusing global navigation satellite system (GNSS) / synthetic aperture radar interferometry (InSAR) observation data. The three-dimensional deformation field modeling method includes: five parts of solving an InSAR line-of-sight velocity field, unifying a spatial reference datum of the GNSS data and the InSAR data, verifying precision of a result of the InSAR data, solving a three-dimensional deformation field having high precision based on a least square method, and analyzing and verifying a result. According to the method, the InSAR data is processed according to a high-precision time series method, a long-wavelength error of the InSAR data is corrected on the basis of a difference of two data spatial reference datums, and the accuracy of the result is ensured through verification of internal coincidence precision.

The invention provides a satellite measurement and control data anomaly detection and intelligent diagnosis early warning method and system, and relates to the technical field of satellite measurement and control, and the method comprises the steps: obtaining multi-source heterogeneous data in a satellite measurement and control system, and carrying out the preprocessing of the multi-source heterogeneous data; constructing a multi-level space-time fusion network model, inputting the preprocessed multi-source heterogeneous data into the multi-level space-time fusion network model, and extracting high-dimensional space-time features; based on the extracted high-dimensional spatial-temporal features, designing a dynamic adaptive anomaly detection algorithm, and performing anomaly detection on the satellite measurement and control data; according to an anomaly detection result, performing intelligent diagnosis by using a pre-constructed multi-level satellite system knowledge graph; based on the intelligent diagnosis result, multi-level early warning information is generated; a distributed edge computing architecture is designed, and partial data processing and anomaly detection tasks are deployed to a ground station and a satellite end; and presenting the generated multi-level early warning information through a visual interface, and automatically triggering a corresponding emergency plan according to the early warning level.

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.

Three-dimensional outdoor positioning can be considerably enhanced by using various 3D imaging techniques, LiDAR ranging measurements of surrounding landmarks, and Inertial Measurement Units (IMUs) reaching accuracies in the centimetres range. These technologies, in conjunction with global navigation satellite systems (GNSS) such as GPS, GLONASS, Galileo, and BeiDou, provide autonomous geo positioning. It is crucial to acknowledge the constraints of dead reckoning, which may result in mistakes in positional precision. Integrating modern imaging and correction techniques can significantly improve the accuracy and dependability of outdoor localisation. The amalgamation of various 3D imaging methodologies with LiDAR enhancements improves the comprehension and ranging of the environment, tackling the difficulties presented by GNSS constraints. The integration of IMUs with satellite systems ensures the provision of continuous traveling positioning data, even in instances of obscured or faulty satellite signals. This comprehensive approach improves outdoor location precision and expands the use of these technologies across other domains, such as autonomous navigation, robotics, and geospatial analysis, resulting in more effective and dependable positioning solutions. The hardware system consists of a swarm of sensors, including GNSS, INS, IMU, LiDAR, stereo imaging, and RGB technologies, which are combined to generate three-dimensional colored representations of natural landscapes and urban environments. This system reconstructs topographical features, enabling detailed mapping and analysis. It produces comprehensive three- dimensional sensory data, including geo-location and POI identification, and incorporates advanced image processing techniques, such as edge detection, for precise spatial awareness and environmental understanding. The system is designed with a compact form factor, making it easily integrated into wearable devices and autonomous vehicles. The integration of these technologies forms a sophisticated network of swarm intelligence, crucial for autonomous vehicle positioning and navigation. Particle swarm optimization (PSO) is used to address non-deterministic navigation, with different coupling fusion methods available. The Neuro-fuzzy PSO AI Positioning and Navigation System generates three-dimensional sensory data, providing detailed information about geographical distribution and dead reckoning areas.

The invention discloses an offshore platform emergency control method based on a Beidou satellite system and related equipment, and relates to the technical field of Beidou satellites.The offshore platform emergency control method based on the Beidou satellite system comprises the steps that Beidou satellite time service signals and real-time meteorological data are obtained, the method comprises the following steps: synchronously acquiring structural deformation parameters, marine environment dynamic parameters and personnel positioning information of an offshore platform, and constructing multi-dimensional data of the offshore platform; fusing and analyzing the multidimensional data of the offshore platform, extracting space-time correlation characteristics through a pre-trained dynamic risk prediction model, constructing a multi-factor causal chain, and determining a platform risk level and accident type prediction result; and triggering graded emergency response according to the accident type prediction result, encrypting and transmitting a corresponding early warning instruction to a preset terminal through a Beidou short message, and executing a corresponding emergency control method. According to the invention, risk prediction and emergency response are carried out through the multi-dimensional data of time-space synchronization, so that the offshore platform can carry out timely emergency control.

The embodiment of the invention discloses a low-orbit navigation enhanced transmitting terminal time difference calibration method, device and equipment and a medium, and relates to the technical field of satellite navigation, and the method comprises the steps: obtaining a low-orbit satellite satellite-borne receiver clock error, ground observation station coordinates, an observation station receiver clock error and other parameters through calculation of observation data of a GNSS satellite; the ground observation station calculates an inter-system deviation between a low earth orbit satellite system corresponding to each low earth orbit satellite and a GNSS system according to the downlink navigation data of each low earth orbit satellite; and selecting one low-orbit satellite as a reference satellite, obtaining the deviation between reference systems, calculating the time deviation of the navigation enhancement transmitting end of the deviation between other systems and the deviation between the reference systems, and compensating the clock error of the corresponding low-orbit satellite based on the parameter. The method can be used for calibrating the relative time difference of each low-orbit satellite navigation signal transmitting end, realizes low-orbit navigation signal time synchronization, is beneficial to improving the positioning, navigation and time service capabilities of a low-orbit satellite system, and provides more accurate services for users.

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.

Systems and methods are described herein for providing network and protocol architectures to achieve efficient high speed data services in an integrated terrestrial-non-terrestrial network (iTNTN). The iTNTN can include at least a non-geostationary orbit (NGSO) satellite system and terrestrial radio access and core network infrastructures based on cellular standards (e.g., 5G). Embodiments specially configure packet-based routing and dynamic cell-CU-DU (cell to centralized unit to distributed unit) association to accommodate dynamically changing LEO satellite locations and other iTNTN characteristics. These and other configurations are used to enable features, including end-to-end IP data and Layer 2 data services, integrated LEO-GEO (low-Earth orbit and geosynchronous Earth orbit) and LEO-MEO (low-Earth orbit and medium-Earth orbit) services, direct UT-UT (user terminal to user terminal) services, and resource efficient multicast services.

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.

Systems, methods, and computer-readable media for aerial localization in the absence of satellite (e.g., global navigation satellite system (GNSS)) signals while flying over heterogeneous environments. The present embodiments provide a training strategy for robust cross-seasonal image matching in complex, real-world environments. The challenge put forward is to successfully localize a UAV flying over both densely and sparsely populated areas by means of image registration. To this end, the focus of the present embodiments can be on devising loss components to a visual recognition network that adequately embeds the diverse feature types on the ground. A normalized error metric can be provided as well in order to render a conceptually different method comparable to the present approach. The present technical contributions can provide robust visual recognition algorithms in challenging natural environments.

The invention relates to the technical field of ocean engineering and remote sensing, in particular to an ocean wave measurement method based on GNSS (global navigation satellite system) wave buoy and atmosphere data fusion, which comprises the following steps: S1, acquiring three-dimensional displacement data of a buoy in real time; s2, collecting atmospheric condition parameters over the target sea area; s3, performing time and space synchronization on the data acquired by the GNSS wave buoy and the data of the atmospheric sensor array; s4, mapping the GNSS wave buoy data and the atmosphere data to a unified feature space; s5, extracting wave characteristic parameters; and S6, establishing a coupling model for quantifying the dynamic influence of atmospheric factors on wave generation, development and propagation. According to the method, the GNSS wave buoy data and the atmospheric condition data are fused, the physical basic model and historical data optimization are combined, high-precision wave characteristic prediction can be achieved, and the defects of a traditional wave measurement method in the aspects of precision, real-time performance and coverage range are effectively overcome.

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.

The invention provides an in-orbit calculation autonomous system for a low-orbit satellite. A satellite system is managed in a mode of dividing satellites in a constellation into a plurality of autonomous groups, a unified management mechanism and an in-autonomous-group management mechanism are constructed to construct a management system of the satellite autonomous groups, and a streaming computing task migration scheme sensitive to a geographic position is constructed to construct an over-the-ground communication optimization scheme of the satellite system. Through a grouping mode, the low-orbit satellites can be divided into a plurality of autonomous groups, and the pressure of management nodes is reduced; a dynamic grouping mechanism is introduced during grouping, so that the balance of calculation load borne by each group in the whole operation period is ensured; a decentralized satellite cluster self-organization and cooperation mode is provided, so that the high availability of the cluster and the robustness of task processing are improved; according to the designed streaming computing task migration scheme sensitive to the geographic position, the expenditure caused by frequent migration of the on-satellite computing task is reduced from two aspects of migration path selection and migration mechanism implementation.

This disclosure is directed to shared antenna tuning. An electronic device may receive a global navigation satellite system (GNSS) tune request to tune a shared antenna to a GNSS signal frequency. The electronic device may then tune the antenna to the GNSS signal frequency and enable a GNSS receiver. The electronic device may also receive a cellular tune request to tune the antenna to a cellular frequency. The electronic device may tune the antenna to a cellular frequency and may deactivate the GNSS receiver or blank the GNSS receiver. In some embodiments, the electronic device may also communicate with a Low Earth Orbit (LEO) satellite. During LEO satellite communication, the electronic device may transmit a signal to blank a GNSS L1 receiver to avoid signal interference with the LEO satellite communication, and activate a GNSS L5 receiver to receive GNSS signals.

A global navigation satellite system (GNSS) spoofing detection and classification technique is provided. An optimization problem is formulated at the baseband correlator domain by using an optimization algorithm such as the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, for example. A model of correlator tap outputs of the intended received signal is created to form a dictionary of pre-computed waveform functions (e.g., triangle-like-shaped functions). Sparse signal processing can be leveraged to choose a decomposition of pre-computed waveform functions from the dictionary. The optimal solution of this minimization problem can discriminate the presence of a potential spoofing attack peak by observing the decomposition of two different code-phase values (authentic and spoofed) in a sparse vector output. A threshold can be used to mitigate false alarms. Furthermore, a variation of the minimization problem can be provided that enhances the dictionary to a higher resolution.

The invention provides a GNSS virtual observation value generation method and system based on real-time precise point positioning, and belongs to the technical field of GNSS positioning, and the method comprises the steps: obtaining ephemeris clock error data and observation data received by a reference station in real time, filling a UCUD PPP observation equation, and solving a non-difference floating point solution of a to-be-estimated parameter of each parameter station; through UPD estimation, non-difference floating point solution ambiguity parameters recover the whole cycle characteristic, and a fixed solution STEC and a fixed solution ZTD are obtained; the STEC is locally fitted into a function about the position, and a regional non-difference ionosphere model is established; the ZTD is locally fitted into a function about the position, and a regional non-difference troposphere model is established; and generating a double-difference atmospheric delay between the main reference station and the virtual reference station based on the non-difference ionosphere model and the regional non-difference troposphere model to obtain a virtual observation value. According to the method, the reliability of GNSS signal atmospheric delay is effectively improved, so that the quality of a virtual observation value is improved.

The invention discloses a positioning processing method suitable for observation data under a GNSS complex condition. The positioning processing method comprises the following steps: step 1, setting residual check thresholds of a pseudo-range positioning observation value and a Doppler velocity observation value; step 2, on the basis of a residual check threshold of a pseudo-range positioning observation value, calculating an optimal estimated value of pseudo-range positioning observation and a corresponding variance-covariance matrix according to residual satellites for satellites with abnormal pseudo-range positioning observation values; step 3, based on a residual error test threshold value of the Doppler velocity observation value and a detection result in the step 2, eliminating satellites with abnormal Doppler velocity observation values, and calculating an optimal estimated value of Doppler velocity observation and a corresponding variance-covariance matrix according to the residual satellites; and step 4, updating the Kalman filtering state vector of the GNSS based on the results of the step 2 and the step 3. The method can improve the estimation precision of the initial values of the position and the speed, and further improves the positioning precision of the GNSS.

Example positioning methods and apparatus are described. One example positioning system includes a first global navigation satellite system (GNSS) antenna, an in-vehicle communication apparatus, and a controller. The first GNSS antenna is configured to obtain satellite data, and transmit the satellite data to the in-vehicle communication apparatus. The in-vehicle communication apparatus is configured to determine first positioning information based on at least one of the satellite data or first inertia measurement unit (IMU) data, and transmit the satellite data to the controller. The controller is configured to determine second positioning information based on the satellite data and second IMU data. Precision of the second positioning information is higher than precision of the first positioning information.

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.

A method and apparatus for mitigation of GNSS-signal interference using an adaptive notch filter (ANF) operates based on signals received from one or more satellites of a Global Navigation Satellite System (GNSS) such as GPS, GLONASS, etc. In one embodiment, an apparatus comprises a notch filter having a tunable zero frequency of a transfer function receives an input signal and generates an output signal. A bandpass filter coupled to the output of the notch filter receives the output signal. An adaptive block is coupled to the bandpass filter and adjusts the notch filter parameters in order to achieve the minimization of a specific cost function.

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.

The invention discloses a method and system for avoiding frequency interference through AI and digital twinning combination, and relates to the technical field of satellite communication, and the method comprises the steps: building a big data satellite frequency interference avoiding system, and collecting the current orbit information and frequency information of a communication satellite; simulation is performed through a preset interference scene prediction model, a satellite adjustment attitude is determined, the preset interference scene prediction model is obtained by training a normal scene and an interference scene of a satellite system based on an AI algorithm and a digital twin model, and the digital twin model comprises an orbit prediction module and an attitude adjustment prediction module which are connected in sequence. The orbit prediction module is used for outputting an orbit prediction value according to orbit information and frequency information, and the attitude adjustment prediction module outputs a satellite adjustment attitude according to the orbit prediction value; therefore, the interference scene of the communication satellite can be predicted in advance through the preset interference scene prediction model so as to adjust the beam direction, and automatic early warning and processing of the interference of the communication satellite are realized.

A method and a device for processing global navigation satellite system (GNSS) validity. The method is performed by a terminal device, and includes: setting a target parameter of radio link failure (RLF), in a case that a GNSS of the terminal device is outdated.

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.