65 results about "Observational error" patented technology

The invention discloses a statistical result-based X-band phased array weather radar multi-beam scanning mode observation error calibration method and system. The method comprises the following steps of: S1, acquiring observation base data of an S-band dual-polarization Doppler weather radar and an X-band dual-polarization phased array weather radar as initial data; s2, performing data quality control on the radar-based data to obtain preprocessed data; and S3, by adopting a different radar time and space sampling volume coordinate matching method and taking an observation value of the S-band radar as a reference, counting a matching difference of the preprocessed data to obtain an observation error of the X-band radar and correcting the observation error.

The invention provides an unmanned aerial vehicle-unmanned vehicle combined formation cooperative control method and system, and relates to the technical field of vehicle-vehicle cooperation, and the method comprises the steps: taking a virtual unmanned aerial vehicle as a leader, taking the unmanned aerial vehicle and the unmanned vehicle as followers in a unified manner, setting a fixed formation offset for each follower, and defining a communication topological relation based on a graph theory; the method comprises the following steps: acquiring an actual measurement state vector of a sensor in real time, processing a current estimation state through a constructed radial basis function neural network observer, outputting a disturbance estimation value in combination with a weight matrix, updating the estimation state according to a core equation, and judging whether to update the weight matrix or not according to an observation error; calculating the expected state of the follower according to the reference trajectory of the leader and the fixed formation offset, calculating the formation error, constructing an event trigger communication condition, judging whether the condition is met or not, enabling the follower to interact the state and the error according to the communication topology only when the condition is met, or else, continuing to use the previous trigger data; and finally, current control input of the follower is calculated through a distributed control law.

The invention provides a multi-platform satellite thermal infrared hyperspectral atmospheric ammonia monitoring method and system, and belongs to the technical field of satellite remote sensing. The method comprises the following steps: collecting thermal infrared hyperspectral data, atmospheric state parameters, earth surface parameters and instrument characteristic parameters of a stationary satellite or a polar orbit satellite; based on a thermal infrared radiation transmission physical mechanism, inputting the preprocessed standardized parameters into a fast radiation transmission forward model to obtain a simulated spectrum consistent with the actually measured data format of a stationary satellite or a polar orbit satellite; constructing a cost function containing observation error constraint and NH3 prior information constraint on the basis of the simulated spectrum and the actually measured spectrum in combination with an optimization estimation theory, solving a minimum value of the cost function by adopting a Levenberg-Marquardt iterative algorithm, and performing inversion to obtain an atmospheric ammonia concentration profile; and performing quality control and column concentration conversion on an inversion result, and verifying the precision by combining multi-source observation data to obtain an atmospheric ammonia concentration data set.

The invention relates to the technical field of attitude control, in particular to a moving object attitude control method based on an improved LKF. Predicting prior attitude estimation at the next moment; calculating a process error covariance matrix; calculating a measurement attitude; calculating a quaternion measurement error covariance matrix; constructing feature vectors of acceleration interference and geomagnetic interference amplitude levels, and calculating an interference amplitude level value by using a Gaussian function model; updating the process error covariance matrix and the measurement error covariance matrix by using the interference amplitude level value; calculating a priori attitude estimation error covariance matrix by using the updated process error covariance matrix; calculating a Kalman gain matrix by using the priori attitude estimation error covariance matrix and the updated measurement error covariance matrix; calculating attitude posteriori estimation at the next moment; and performing attitude estimation at the next moment. According to the method, the problems of insufficient estimation precision and poor interference suppression capability of the attitude observation error covariance matrix of the existing LKF are solved.

The invention discloses a high-altitude wind field hourly forecasting circulation assimilation correction method for guaranteeing the safety of an air route of an aircraft, belongs to the field of aerospace meteorology, and solves the problem of how to improve the vertical layering fineness and resolution in a WRF mode forecasting result. Comprising the following steps: carrying out observation data preprocessing and quality control, and setting a background field error covariance and an observation error covariance; constructing a detection data assimilation scheme; judging weather conditions at an initial moment, and inputting observation data bound at a corresponding moment for assimilation to obtain an observation field; assimilating the observation field and the background field to generate an analysis field, and starting mode numerical forecasting by using the analysis field to obtain a high-altitude wind field hourly forecasting result; comparing with observation data, correcting and evaluating a high-altitude wind field hourly forecast result, and adjusting a detection data assimilation scheme to carry out cyclic assimilation correction until a forecast result meeting a preset standard of an aircraft route and an optimal detection data assimilation scheme are obtained; the accuracy of high-altitude wind forecasting is improved, and the safety of the air route of the aircraft is guaranteed.

The invention discloses a meteorological radar grading identification method combined with a meteorological numerical model, and relates to the technical field of intelligent meteorological perception, and the method comprises the steps: generating a unified initial state vector in a preset calibration region through employing historical typhoon similar sample statistical information, and achieving the cold start calibration under the condition of no radar data; then distributing a radar extrapolation field within two hours and a numerical mode field weight after two hours based on a time gating function, respectively calculating an observation residual error covariance matrix and an observation error covariance matrix, and carrying out joint correction on an amplitude component and a deformation component by adopting Kalman updating; through mode CLDAS field large-scale extrapolation, spatial resolution refinement, terrain factor correction and radar extrapolation field high-precision fusion, a fusion precipitation field is obtained, a hierarchical graph is generated based on an adaptive quantile threshold value, and iterative updating is completed; according to the method, through unified initial state vector generation of historical typhoon similar sample statistics, the stability of water field initialization under a radar-free condition is realized.

The invention discloses an oil reservoir history fitting method based on graph lasso and set smooth multi-data assimilation, and relates to the technical field of oil and gas field development. The method comprises the following steps: firstly, acquiring to-be-optimized model parameters and observation data of a target oil reservoir, constructing an observation error covariance matrix and initializing a model parameter set, performing numerical simulation by utilizing an oil reservoir numerical simulator to obtain prediction data, constructing a joint state matrix, performing dimensionless standardization processing on the joint state matrix to obtain an empirical correlation coefficient matrix, and then, performing optimization on the joint state matrix. And executing a graph lasso algorithm combined with an extended Bayesian information criterion to obtain an optimal dimensionless sparse precision matrix, extracting correlation coefficient sub-blocks required by Kalman updating from the optimal dimensionless sparse precision matrix based on a Scherr's theorem, obtaining a robust data auto-covariance matrix through a reverse reduction physical quantity outline, calculating Kalman gain in combination with an observation error covariance matrix, and calculating a Kalman filter. And the model parameter set is updated until the preset condition is met, the reservoir history fitting model parameter set is output, and the stability and precision of reservoir automatic history fitting are improved.

The invention discloses an atmospheric ocean multi-source data high-precision assimilation method and device, and relates to the technical field of ocean multi-source data assimilation, and the assimilation method comprises the steps: collecting multi-source observation data of a typhoon region of the South China Sea; preprocessing the data; decomposing the observation field into different scales by using wavelet transform; acquiring a background field from the numerical model, interpolating the background field to a grid consistent with observation, projecting the background field to an observation space, and performing multi-scale decomposition to enable the background field to correspond to an observation scale; calculating a difference vector of observation and background fields on each scale, and dynamically calculating an adaptive weight of each point on each scale according to an observation error, a background error and a current deviation; and constructing a cost function, solving through an optimization algorithm to obtain an analysis field, and outputting a final analysis field. According to the method, a multi-scale decomposition and self-adaptive regularization cooperation mechanism is introduced, so that high-precision dynamic fusion of the multi-source observation data and the numerical model can be realized.

The invention discloses a microwave-quantum sensing slope full-dimensional monitoring method and system, and relates to the technical field of slope monitoring, and the method comprises the steps: obtaining a multi-source monitoring data set of a slope, obtaining a displacement-stress coupling coefficient according to the correlation between a displacement variation and a stress variation, obtaining a stability index in combination with a data variance, and generating a characteristic factor; further obtaining a characteristic index under the geological category; according to the characteristic indexes and the stability indexes of the monitoring points, different indexes are obtained to classify the monitoring points, the observed quantity and the state quantity of the monitoring subareas of the to-be-processed points are obtained, and an observation error covariance matrix is calculated; then Kalman filtering is carried out, a denoised current state value and a predicted value at the next moment are obtained, a coupling coefficient and a stability index are combined as input, a landslide risk level is obtained through a multilayer radial basis network model, and an emergency scheme corresponding to the landslide risk level is given. According to the invention, the slope full-dimensional early warning is realized by combining the coupling relationship between the earth surface and the underground.

The invention provides a frequency-locked phase-locked loop method, which is based on a synchronous rotating coordinate system, and adopts an angular frequency error and a phase angle error to carry out frequency locking and phase locking. According to the technology, a low-pass filter is adopted to observe a power grid voltage synchronous rotating coordinate system component udq to obtain an observation error edq and an observation value # imgabs0 #, the conjugate # imgabs1 # of the observation value is calculated, and the observation error edq is multiplied by the conjugate # imgabs2 # of the observation value to obtain an expression containing angular frequency error information; multiplying an imaginary part xaI containing an angular frequency error information expression by a normalization coefficient gamma and a transfer function f (s) for pole-zero cancellation to obtain an angular frequency error omega e, multiplying an actually measured value of a direct-axis component or a quadrature-axis component of the power grid voltage by a proportionality coefficient K, or multiplying an observed value by the proportionality coefficients K and f (s) to serve as a phase angle control signal epsilon, and calculating the phase angle of the power grid voltage according to the phase angle control signal epsilon. And after the added value is input into the integrator with the transfer function of # imgabs3 #, an angular frequency observation value # imgabs4 # is output, after the angular frequency observation value # imgabs5 # is input into the integrator with the transfer function of # imgabs6 #, a phase angle observation value # imgabs7 # is output, and finally frequency locking and phase locking are achieved.

The invention discloses a pulse signal source tracking method based on a robust Kalman filter. The method comprises the following steps: reading a target state vector and error covariance matrix at a previous moment, and observing a noise covariance matrix; a variational Bayesian parameter before iteration, a target state vector and an error covariance matrix are initialized; starting iteration, and calculating the quadratic form sufficient statistics of the observation error and the forecast error; updating variational Bayesian parameters; updating the forecast error covariance matrix and the observation noise covariance matrix; calculating a target state vector and an error covariance matrix of the iteration; when the variation of the target state vector of the current iteration and the target state vector of the last iteration is smaller than the tolerance, ending the iteration, and outputting the target state vector at the current moment, the error covariance matrix and the observation noise covariance matrix; compared with a deterministic integral sampling method, the method provided by the invention adopts adaptive volume sampling to realize higher tracking precision under lower calculation cost.

The invention discloses a satellite height measurement data optimal interpolation fusion method based on cross calibration and spectrum analysis, and the method comprises the steps: carrying out the sea surface height anomaly calculation and power spectrum analysis, and calculating the sea surface height anomaly of a satellite altimeter according to the precise orbit determination coordinates of a satellite and the distance measurement value of the altimeter; the related scale of adjacent observation points of the to-be-fused satellite altimeter is obtained through the power spectrum analysis of the sea surface height anomaly of the to-be-fused satellite altimeter; cross calibration: performing cross calibration on sea surface height anomaly of the fusion satellite altimeters by using the sea surface height anomaly of the reference satellite altimeter to obtain system deviation in the to-be-fused satellite altimeters; performing self-crossing calibration by using sea surface height anomaly of the satellite altimeter to be fused to obtain a diagonal element of an observation error covariance matrix; constructing a covariance matrix, wherein a non-diagonal element of the observation error covariance matrix is a product of a standard deviation at the two measurement points and a correlation function of the two measurement points; and fusing the fused satellite altimeter and the background field data by using an optimal interpolation method.

The application discloses a target initial orbit determination method and device based on spatial filtering velocity measurement, which comprises the following steps: acquiring a star position image; determining an observation target according to the star position image, and establishing a mapping relationship between a measurement coordinate and an angle position of the observation target; modulating the star position image to obtain a distribution signal of target brightness of the observation target changing with time; acquiring a velocity of the observation target according to the distribution signal of the target brightness changing with time; obtaining an angular velocity and an acceleration of the observation target according to the velocity of the observation target through the mapping relationship between the measurement coordinate and the angle position of the observation target; and obtaining a position vector and an initial orbit velocity of the observation target relative to the earth center through an initial orbit determination algorithm. Compared with a traditional initial orbit determination method, the application directly measures the angular velocity of the target, reduces the coupling between the angle position error and the angular motion quantity, improves the initial orbit determination precision of the target under the observation error condition, and realizes the rapid orbit determination of the target.

The invention relates to a cycle slip processing method, device, equipment and medium, and the method comprises the steps: obtaining the observation error data of each satellite according to the difference between the double-difference carrier observation data of each satellite in a plurality of satellites of a target carrier frequency and the double-difference satellite-to-earth distance data of each satellite based on a current epoch and a previous epoch for a reference station and a monitoring station; the receiver clock drift deviation between the base station and the monitoring station is estimated based on the observation error data of the two first satellites, clock drift deviation data is obtained, and the two first satellites are the two satellites with the minimum observation error data difference value in the multiple satellites; and on the basis of the double-difference carrier observation data of a second satellite in the plurality of satellites, subtracting the double-difference satellite-ground distance data and the clock drift deviation data of the second satellite, and determining single-difference ambiguity incremental data so as to perform single-frequency cycle slip processing on the target carrier frequency of the current epoch on the base station and the monitoring station. According to the invention, single-frequency cycle slip processing can be efficiently and jointly carried out on double stations without the help of an external sensor.

The invention discloses a rolling angle estimation method and system for a rotary guided projectile, a medium and a product, and relates to the field of projectile guidance and control. According to the technical scheme, firstly, the guidance control system collects a rotation speed signal and a speed change signal after the rotary guided projectile is ejected out of a chamber, and then an accurate kinematics equation set is constructed; secondly, the guidance control system separates the basic rotating speed component and the fluctuation rotating speed component and then combines the components to obtain an actual rotating speed value, a rotating speed estimation value is obtained by constructing a state prediction equation, and then an observation error value is obtained. Then, the guidance control system calculates a mahalanobis distance based on the observation error value, the error covariance matrix and the measurement noise covariance matrix, and determines a weight coefficient to obtain a filtering rotating speed sequence; and finally, the guidance control system performs numerical integration on the filtering rotating speed sequence in a preset time interval to obtain a final rolling angle estimated value. Therefore, the instantaneous rolling angle of the rotary guided projectile in the flight process can be accurately estimated.

This invention relates to the field of power electronics technology, specifically to a method and system for monitoring the insulation of DC charging piles. The method includes the following steps: real-time monitoring of branch voltage deviation from the busbar; adjusting measurement compensation parameters; correcting the measured value of branch insulation resistance; identifying leakage current drift; locating the leakage branch and triggering relay control; sending alarm information; and generating a charging pile monitoring record. In this invention, the consistency of branch voltage measurement is improved by combining branch voltage deviation trends with voltage compensation parameters. Observational errors are corrected by comparing insulation resistance fluctuations, optimizing measurement accuracy. Current deviation compensation under no-load conditions eliminates sensor zero-point drift interference. Leakage branch identification is achieved by combining current jump amplitude and voltage direction, enhancing location accuracy. Relay control and event information linkage are used to form a closed loop between alarm response and equipment status recording, improving the real-time performance and completeness of system monitoring.

This application discloses a one-dimensional variational inversion method and apparatus for GNSS occultation data. The method includes background field preparation: interpolating temperature, air pressure, and water vapor pressure from the grid field to the latitude and longitude of the occultation data; simultaneously, interpolating the liquid water content and ice water content in the clouds to the latitude and longitude of the occultation data to obtain the background field profile required for inversion; and inputting observation data and background field: inputting the occultation data, background field profile, observation error covariance matrix, and background field error covariance matrix into the one-dimensional variational inversion system. By adding liquid water content and ice water content terms to the observation operator, the deviation between the observation data and the background field data is calculated, and the distribution characteristics of the deviation are statistically analyzed. After adding liquid water and ice water, the average refractive index deviation between the two types of data is significantly reduced.

The invention relates to a surveying traverse auxiliary teaching example generation method and system, which can realize horizontal angle observation, vertical angle observation, distance observation, automatic generation of traverse measurement examples, observation records and handbooks and automatic inspection of calculation results, and randomly generate different traverse examples based on traverse point coordinate results. The invention also discloses a traverse calculation method of the traverse measurement example. The system randomly generates different traverse calculation examples, reversely calculates traverse derivation theoretical numerical values according to traverse results, simulates observation errors existing in actual observation, reversely calculates traverse measurement example observation values and automatically generates a traverse observation handbook and a calculation handbook, and students perform traverse observation forward calculation through the observation handbook. And after the calculation result is submitted, the system automatically checks and scores. According to the invention, the problem of single traverse measurement example in the measurement textbook is effectively solved, and students can independently master the traverse measurement interior work calculation method.

The application discloses a TLE target library matching method and system for radar observation data, and belongs to the field of space target monitoring; the method comprises the following specific steps: S1, using an SGP4 model to derive and store position information of all targets in a TLE library; S2, converting radar measurement data into position coordinates of targets in an inertial system; S3, using an extended Laplace algorithm to perform initial orbit determination on a measurement arc segment; S4, screening targets in the TLE library according to the orbit determination result; S5, performing coarse matching of an observation arc segment and a space target by taking position mean square error as a criterion, and obtaining candidate targets; and S6, considering observation error and TLE orbit error, and determining a matching result according to covariance analysis; the application reduces the calculation amount of orbit prediction by using Lagrange interpolation technology, and screens TLE by using initial orbit determination results, so that unnecessary matching calculation can be avoided, coarse matching and fine matching are combined, the target matching speed can be ensured, and the matching precision can also be ensured.

The invention relates to an optimal interpolation assimilation method for adaptive observation station space distribution. The method comprises the following steps: (1) obtaining a background error covariance matrix, an observation error covariance matrix, an optimal weight matrix, a kth observation station and a background field; (2) carrying out first iteration on the background field, and carrying out weighted averaging to obtain an observation operator of initial iteration at a grid point in the background field; (3) according to the observation operator of the initial iteration, calculating to obtain a background field of the first-generation iteration; iteration is carried out in the same manner until the absolute value of the observation increment is smaller than a small quantity, iteration is stopped, and at the moment, an observation operator dynamically changing along with the space is obtained and used for calculation of an analysis field. The method is simple, high-precision atmosphere reanalysis data can be effectively constructed, the precision of the initial field of the numerical weather forecasting mode is improved, and the method is suitable for construction or reconstruction of data sets in the fields of meteorology, new energy, aviation ocean and the like.

The application provides a diversion tunnel surrounding rock mechanics parameter inversion method and device based on multi-target learning, and the method comprises the following steps: a sampling method containing directional number disturbance low difference sampling sequence is adopted to construct a parameter-response sample data set of the diversion tunnel surrounding rock; a multi-penalty item loss function is used for model training on a multi-target machine learning algorithm to construct a diversion tunnel surrounding rock response multi-output proxy model; the multi-penalty item loss function comprises an observation error item, a time sequence monotonic-stable penalty item and a cross-measurement point collaborative penalty item; the proxy model is used for parameter inversion to generate an optimal mechanics parameter solution; the precision and generalization ability of the surrounding rock mechanics parameter inversion can be significantly improved under the condition of a small sample; the physical consistency of the inversion result is ensured through time sequence monotonic and cross-measurement point collaborative constraints; the method has good noise resistance and asynchronous data robustness, can realize optimal parameter output under multi-target weighting, and forms a closed-loop verification from inversion to numerical review.

The invention provides a high-speed wind tunnel free flow correction method based on total temperature regular data assimilation, and the method comprises the steps: (1) determining a wind tunnel design working condition, and determining a test model and an observation physical quantity; obtaining observation data on the surface of the test model, and giving noise of the observation data; (2) constructing an incoming flow working condition sample set; (3) constructing a prior sample set; (4) determining a total temperature regular value and a weight matrix; (5) carrying out TEnKF optimization, constructing an observation error covariance matrix according to observation data noise, constructing a sample covariance matrix according to a prior sample, calculating a Kalman gain matrix, and updating to obtain a posterior state vector; (6) calculating the difference between the priori state vector and the posteriori state vector; and (7) obtaining corrected wind tunnel incoming flow parameters. According to the method, the assimilation algorithm is ensured to correct the incoming flow correction result in a reasonable physical range, and the incoming flow working condition of the hypersonic wind tunnel can be effectively corrected.

The application relates to the technical field of data processing, and discloses a satellite observation data three-dimensional variational assimilation method based on channel correlation. The method comprises the following steps: obtaining a channel correlation statistical matrix through statistical analysis and processing; constructing a non-diagonal observation error covariance matrix based on the matrix; decomposing the matrix into covariance sub-matrices by using a matrix block algorithm; quickly solving the observation error weight matrix by using a parallel numerical inversion algorithm; and finally performing variational assimilation calculation and processing on a three-dimensional atmospheric state vector to obtain atmospheric state estimation data that fuses channel correlation. The application solves the technical problems of ignoring channel correlation and low calculation efficiency in the existing satellite observation data three-dimensional variational assimilation method.

The invention relates to the technical field of data processing, and discloses a satellite observation data three-dimensional variational assimilation method based on channel correlation. The method comprises the following steps: obtaining an inter-channel correlation statistical matrix through statistical analysis processing, constructing a non-diagonalization observation error covariance matrix based on the matrix, decomposing the matrix into covariance sub-matrixes by using a matrix partitioning algorithm, and rapidly solving through a parallel numerical inversion algorithm to obtain an observation error weight matrix. And finally, variational assimilation calculation processing is carried out on the three-dimensional atmospheric state vector to obtain atmospheric state estimation data fused with channel correlation. The technical problems that in an existing satellite observation data three-dimensional variation assimilation method, correlation between channels is ignored, and the calculation efficiency is low are solved.

The application discloses a cross-dimension multi-source satellite altimeter sea level anomaly joint estimation method and system, and relates to the field of ocean remote sensing.The joint estimation method comprises the following steps: obtaining multi-source observation data of a target sea area about a target period, forming a corrected joint observation data set through cross-dimension data processing and reconstruction operation and bias correction operation, and mapping the corrected joint observation data set to a corresponding regular space-time grid of the target sea area; based on a preset space-time search radius, constructing an observation operator, a background covariance matrix and an observation error covariance matrix corresponding to each grid point in the regular space-time grid, and generating a regular gridded sea level anomaly product of the target sea area about the target period through an optimal interpolation method without performing explicit system error correction.The application can consider large-scale background consistency and small and medium-scale structure resolution capability on multiple spatial scales, and improves the spatial continuity, statistical consistency and overall stability of the sea level anomaly estimation result.

The invention discloses a proportional-integral state estimation method of a nonlinear wind turbine system based on dynamic summation type event triggering. The method comprises the steps of 1, describing nonlinear dynamic characteristics of a wind turbine system based on a T-S fuzzy model method, and establishing a state space model of the wind turbine system; 2, in order to save network resources, an event triggering mechanism based on dynamic summation is established, and redundant triggering is reduced; 3, designing a proportional-integral estimator to estimate the system state due to all the system states which are difficult to measure in actual engineering; and 4, in combination with the T-S fuzzy wind turbine system and the proportional-integral estimator, establishing a closed-loop estimation error system, and performing state estimation on the wind turbine system by designing a state estimator gain matrix. Compared with a traditional event triggering mechanism based on current information, the event triggering mechanism based on historical sampling data is provided, internal variables are introduced into triggering conditions, the triggering interval is further expanded, and more network resources are saved; compared with an event triggering mechanism which only adopts a static threshold, a dynamic triggering threshold is designed, the threshold can be adaptively adjusted along with the evolution of the system, and the flexibility of the triggering mechanism is improved. In addition, compared with a traditional proportional estimator, the method introduces an integral term of a state observation error to improve the precision of system state estimation, and has a certain engineering application value.

A fast steering mirror angle automatic calibration system and method based on a PSD sensor belong to the technical field of fast steering mirror surface calibration. It solves the technical problems of low efficiency and accuracy in the conventional fast steering mirror surface angle calibration. By utilizing the high resolution and fast response characteristics of the PSD, the present invention proposes an efficient automatic calibration method. A narrowband filter is used in combination with power supply ripple to remove the influence of noise, effectively suppressing the problem that the position calculation of the PSD sensor is vulnerable to optoelectronic noise interference and improving the measurement stability. During the calibration process, real-time data filtering and automatic acquisition are adopted to reduce manual intervention and the influence of the observation errors brought by it. Compared with the traditional calibration method, this method does not require an autocollimator, has a faster calibration speed and lower cost. The present invention breaks through the limitation of the application of the PSD as a linear displacement sensor in high-precision angle measurement, providing an efficient and low-cost solution for the calibration of the fast steering mirror angle.

The present application belongs to the technical field of wireless signal positioning, and discloses a ground-based radiated source TDOA positioning method based on the constraint of the earth ellipsoid. First, the TDOA observation quantity about the ground-based radiated source is obtained by using multiple adjacent space observation platforms, and is converted into a distance difference observation quantity. Then, a scalar product matrix is constructed by using the distance difference observation quantity, and a pseudo-linear observation equation is constructed based on the same. Next, the influence of the distance difference observation error and the prior observation error of the observation platform position vector on the pseudo-linear observation equation is quantitatively analyzed by using a first-order error analysis method, and the singularity problem of the pseudo-linear observation error covariance matrix is avoided by using matrix QR decomposition, so that an optimal weighting matrix is obtained. Then, by using the earth ellipsoid constraint to which the ground-based radiated source is subjected and the algebraic characteristics of the auxiliary variable, a positioning optimization model containing double quadratic equality constraints is constructed. The present application utilizes the earth ellipsoid constraint to which the ground-based radiated source is subjected, and effectively improves the positioning precision.

The invention relates to the technical field of electric power, and discloses a synchronous positioning and map construction method suitable for high-altitude electric power inspection, which comprises the following steps: constructing the position attitude and environmental map characteristics of an inspection robot into a joint state vector, and performing recursive estimation based on extended Kalman filtering; obtaining the observed quantity of each sensor and carrying out weighted fusion on the observed quantity to obtain a fusion observation result; introducing an adaptive noise estimation model, wherein the estimation model adjusts a covariance matrix of state noise and observation noise of extended Kalman filtering according to the observation error and the prediction error; evaluating an uncertain area in the map according to the updated environment map features, and performing local optimization on the uncertain area according to the observed covariance matrix and the error between the observed value and the predicted value of the map features. The improved SLAM algorithm provided by the invention can adapt to a complex high-altitude environment by optimizing sensor data fusion, enhancing robustness and improving a map updating strategy.

The application relates to a method for constructing an inertial navigation system parameter error model based on gravity disturbance. The method comprises the following steps: constructing an accelerometer error model and a gyro observation error model by using random constant zero bias of an accelerometer and random constant drift of a gyro and zero mean random white noise; decomposing measured gravity into gravity disturbance and gravity disturbance measurement error, and constructing velocity error by using the accelerometer error model, the gravity disturbance and the gravity disturbance measurement error; expanding the velocity error to obtain eastward velocity error and northward velocity error; equivalently setting the carrier height of the inertial navigation system to zero, and calculating position error by using the eastward velocity error and the northward velocity error; calculating attitude error by using the gyro observation error model and misalignment angle; and constructing a navigation system parameter error model according to the velocity error, the position error and the attitude error. The method can improve error compensation precision in the navigation process.