263 results about "Measurement equations" patented technology

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 discloses a power distribution network topology identification method and device, and the method comprises the steps: obtaining the original measurement information of a power distribution network; based on the original measurement information, constructing a mixed integer nonlinear programming topology measurement identification model based on a branch current method; utilizing the target data physical fusion driving linearization model to perform linearization processing on a nonlinear constraint and measurement equation to be used in the topology measurement identification model of the mixed integer nonlinear programming based on the branch current method to obtain a processing result; and constructing a topology measurement identification model of mixed integer linearization programming based on a branch current method by using a processing result so as to output a topology identification result of the power distribution network. Therefore, the problems that in the related technology, the efficiency of maintaining the topology file based on a manual and data statistics method is low, a topology error identification model based on an optimization class method is poor in adaptability and low in accuracy, the accuracy of topology identification is reduced, and the operation stability of a power distribution network is reduced are solved.

The invention belongs to the technical field of orthopedic surgery navigation, discloses a communication and positioning integrated orthopedic surgery navigation method and system based on multi-dimensional perception, and aims to solve the problems that in an existing orthopedic navigation technology, optical navigation is prone to being shielded, multipath interference of UWB positioning is serious, and real-time performance is insufficient. Comprising the steps of obtaining measurement values including positioning data of a UWB positioning module, ranging data of a laser ranging module and an attitude angle of an IMU module; according to the method, extended Kalman filtering is adopted, positioning data of a UWB positioning module, ranging data of a laser ranging module and an attitude angle of an IMU module are taken as state vectors, dynamic adjustment of credibility is realized by adjusting a process noise matrix of a state equation and a measurement noise matrix of a measurement equation according to task characteristics, and the credibility of the UWB positioning module is improved according to data of different credibility. And resolving the pose state of the tail end of the surgical instrument. The method has the advantages of high positioning precision, high response speed, high environmental adaptability and high resource efficiency.

The invention relates to the technical field of magnetic detection, in particular to a target positioning method based on geomagnetic anomaly and an axis frequency magnetic field. Comprising the following steps: S1, preparing before entering water; s2, approaching a target; s3, target detection: when no target appears, an observation signal is composed of an environment magnetic field, and the energy value of the part is very small; once the target appears, the energy of the observation signal can be continuously and obviously increased, when the energy value is greater than a preset judgment threshold, the target point is judged as a suspected target point, and if the suspected point is continuously detected for a specified time, the existence of the target is confirmed; s4, combined positioning: under a Kalman filtering framework, enabling the target to be equivalent to a magnetic dipole and an electric dipole, and correcting a position predicted by a target state equation by combining measurement equations of the two models, thereby realizing combined positioning of the target and obtaining more accurate target position information; and S5, verifying and outputting a positioning result. The method has the advantages of improving the positioning precision, enhancing the anti-interference capability and the like.

The invention discloses a power distribution method under cooperative tracking of a networking joint radar communication system, and the method comprises the steps: building a target state according to the position of a radar at each moment and a state vector of a target at each moment; establishing a measurement equation according to the measurement information of the target measured by the radar; establishing a communication model according to the distance between the radar and the fusion center and the reference path loss; a posterior Cramer-Rao lower bound of each target is constructed; constructing a power distribution model according to the posterior Cramer-Rao lower bound and the communication model; and solving the power distribution model by using convex optimization to obtain a power distribution result of the networking joint radar communication system. According to the method, radar tracking transmitting power distribution and communication power distribution are optimized, the posterior Cramer-Rao lower bound of target tracking is minimized on the premise that the lowest rate requirement of a communication system is met, the optimal balance of perception-communication efficiency is achieved, and the communication efficiency is improved. And the tracking capability of the networking joint radar communication system on the target is improved while the power waste is reduced.

On the basis of tight coupling of the SINS and the USBL, a dual-hydrophone differential model is provided, constant errors in USBL measurement are eliminated, and the influence of slant range noise on navigation precision is relieved; a measurement equation of a dual-hydrophone differential model is established, so that the precision and reliability of the integrated navigation system are improved; a robust iterative Kalman filtering algorithm based on approximate least-square estimation is introduced, measurement dimensions are comprehensively evaluated, measurement noise covariance is dynamically adjusted, and the numerical stability and robustness of the algorithm are improved. According to the method, whether the measured data at the current moment is an abnormal value or not is judged according to previous data, the robust weight is reasonably distributed to the measured data, the adverse effects of subsequent steps such as state estimation are reduced through a method of reducing the weight of the abnormal value, and normalization operation is carried out on the measured residual error according to the adverse effects, so that the residual error distribution is more regular, and the accuracy is improved. And the updating result of the measurement noise covariance is further optimized, so that the precision of the whole Kalman filtering algorithm is improved.

The invention provides a Q / R dual-adaptive hybrid quantum filtering method for an OTFS (Over the The Over the File System) communication and inductance integrated system, and the method comprises the following steps: constructing an OTFS communication and inductance integrated signal model, and obtaining a target measurement vector; establishing a state vector for describing a target motion state, and constructing a state transition equation and a measurement equation; the method comprises the following steps: constructing a Q / R mixed quantum adaptive adjustment module based on dual-channel features to construct a dual-channel feature input vector, generating a dual-path adjustment factor through a mixed quantum neural network, and constructing an adaptive Q matrix and an adaptive R matrix; and performing adaptive state prediction and updating at each time step by using the adaptive Q and R matrixes constructed in the step 3 to realize tracking of the motion state of the target. According to the method, the hybrid quantum neural network is introduced for driving, collaborative optimization of filter parameters is achieved, and therefore the precision, robustness and response speed of target tracking are remarkably improved in a complex dynamic noise environment.

The present invention discloses a visual inertial odometry method and system based on transformation error state, and relates to the technical field of visual inertial odometry optimization. The technical points of the present invention include: estimating posture information using a transformation extended Kalman filter, including: establishing a system continuous-time motion model and measurement equation based on real-time acquired visual and IMU data; establishing a linearized error state system based on the system continuous-time motion model and measurement equation; designing a linear time-varying transformation, and using the linear time-varying transformation to transform the linearized error state system into an error state system in which the system state is independent of the system's unobservable subspace; performing state estimation based on the transformed linearized error state system to obtain estimated posture information. The present invention proposes a transformation-based method to solve the inconsistency problem in VINS, alleviates the observability mismatch problem, and ensures that the visual inertial odometry using the transformation extended Kalman filter has consistent estimation results.

The invention belongs to the field of attitude estimation, and discloses a SigKAN network assistance-based Kalman filtering attitude estimation method, which comprises the following steps of: establishing a discrete time state equation and a measurement equation, and modeling a disturbance component and a reference component; a SigKAN model is trained; a SigKAN model is used to predict the dynamic characteristics of the noise; removing a disturbance component by using a Kalman filtering algorithm, and initializing state variable priori estimation and a covariance matrix thereof; calculating a residual error and a covariance thereof by using the measurement information, calculating a Kalman gain, and obtaining a state variable estimation value and a covariance matrix thereof; the gravitational acceleration and the geomagnetic field serve as observation vectors, a reference vector is obtained, attitude calculation is conducted through a QUEST algorithm, and an attitude estimation quaternion is obtained; and obtaining attitude estimation at all moments. According to the method, the dynamic characteristics of disturbance are learned by adopting the SigKAN network, disturbance estimation is carried out by assisting the filter in a mode of dynamically adjusting process noise covariance, and the anti-interference capability of a traditional Kalman filter is enhanced.

The present invention discloses an AI-based intelligent building operation analysis method and system. The method includes: collecting environmental system data, energy consumption system data, and personnel flow system data of a building through sensors, and performing time alignment on the three types of system data through time axis mapping to obtain an alignment matrix; performing adaptive wavelet packet decomposition on the alignment matrix to obtain extended features, and constructing a feature tensor based on the extended features; using the features in the feature tensor as observation variables to construct a latent variable model, where the latent variable model calculates the influence weight of the latent variable on the observation variable through a measurement equation, and performs dynamic coupling degree calculation based on the influence weight; using the features in the feature tensor as nodes, and using the product of transfer entropy and dynamic coupling degree as edge weights to construct a causal network, and performing root cause analysis on the causal network through the PrefixSpan algorithm. The present invention solves the problem that existing building operation analysis technologies cannot comprehensively consider the dynamic interaction between multi-system data.

The embodiment of the invention discloses a deviation angle-considered integrated navigation system attitude estimation method, and relates to the technical field of inertial navigation / satellite navigation integrated navigation, the method is applied to a carrier, and the method comprises the following steps: obtaining observation data through a GNSS receiver to obtain first attitude measurement data; obtaining second attitude measurement data according to the measurement data of the inertial navigation unit in combination with the motion state; constructing an integrated navigation system state equation considering the deviation angle, constructing an integrated navigation system measurement equation based on the first attitude measurement data and the second attitude measurement data, and obtaining an attitude estimation model; initial parameters are obtained based on the observation data and the measurement data, the attitude estimation model is recurred through Kalman filtering, and the attitude angle of the carrier is output. According to the method, by means of the assistance of the speed information provided by the GNSS, the influence of the radial acceleration and the centripetal acceleration to the ground is considered, the method for determining the accelerometer attitude and estimating the deviation angle in real time in the dynamic environment is realized, and the system error of attitude determination is reduced.

The invention discloses a Ga-68 activity direct measurement method which comprises the following steps: S1, preparing Ga-68 samples including a measurement sample and a blank sample; s2, establishing a mathematical model of the detection efficiency of the Ga-68 sample, including the detection efficiency epsilon D that double tubes of a single particle conform to D and the detection efficiency epsilon T that three tubes of the single particle conform to T; s3, establishing measurement equations based on the relationship between the measured total count and the mathematical model of the Ga-68 sample detection efficiency, including the measurement equations corresponding to the total detection efficiency epsilon total, D of the sample under the condition that double tubes conform to D and the measurement equations corresponding to the total detection efficiency epsilon total, T of the sample under the condition that three tubes conform to T; and S4, calculating the Ga-68 activity based on the measured sample count and radionuclide detection efficiency. The method provided by the invention meets the requirements of simple and convenient sample preparation, recyclable nuclide and low uncertainty.

The invention discloses an AI-based intelligent building operation analysis method and system, and the method comprises the steps: collecting environment system data, energy consumption system data and people flow system data of a building through a sensor, and carrying out the time alignment of the three types of system data through time axis mapping, and obtaining an alignment matrix; performing adaptive wavelet packet decomposition on the alignment matrix to obtain extended features, and constructing a feature tensor based on the extended features; the features in the feature tensor serve as observation variables to construct a hidden variable model, the hidden variable model calculates the influence weight of the hidden variables on the observation variables through a measurement equation, and dynamic coupling degree calculation is conducted based on the influence weight; features in the feature tensor serve as nodes, the product of the transfer entropy and the dynamic coupling degree serves as an edge weight, a causal network is constructed, and root cause analysis is conducted on the causal network through a PrefixSpan algorithm. According to the method, the problem that dynamic interaction among multi-system data cannot be comprehensively considered in an existing building operation analysis technology is solved.

The invention discloses a transfer alignment method and transfer alignment equipment based on a Lie group left invariant error model, and belongs to the field of inertial navigation systems. The method comprises the following steps: firstly, establishing a left invariant error state model between a master inertial navigation system and a slave inertial navigation system based on the Lie group theory; secondly, constructing an attitude and speed error differential equation considering the lever arm effect and the installation error angle influence at the same time, then selecting attitude and speed measurement defined under the Lie group, and establishing a corresponding measurement equation; and finally, designing an unscented Kalman filter according to the nonlinear state equation and the measurement equation, and carrying out joint estimation on parameters such as a misalignment angle, a speed error, a lever arm vector, an installation error angle and inertial navigation zero offset. The transfer alignment method provided by the invention not only fully considers the actual lever arm error between the main inertial navigation system and the sub inertial navigation system, improves the integrity of system error modeling, but also adopts the local navigation coordinate system as the reference coordinate system, is closer to the actual engineering requirements, and has good universality and engineering application prospects.

The invention discloses an installation error calibration method based on Student's T distribution and variational Bayes, which comprises the following steps: firstly, constructing an installation error calibration geometric model of an SINS / USBL system, defining coordinate systems, establishing an attitude transfer matrix between the coordinate systems, and designing a state equation and a measurement equation by taking installation error angles in three directions as state variables; time updating and measurement updating are carried out based on a Kalman filtering framework; the method comprises the following steps of: embedding Student's T distribution into a variational Bayesian filtering framework, alternately updating distribution parameters of a state variable, a noise covariance and an auxiliary variable through a variational iterative optimization process, maximizing a variational lower bound until convergence, and outputting an optimized installation error angle estimated value. According to the method, acoustic measurement noise is modeled by using the heavy tail characteristic of the SINS / USBL combined system, the interference of outliers on installation error angle estimation is remarkably inhibited, the positioning accuracy of the SINS / USBL combined system in a complex underwater environment can be effectively improved, and the calibration robustness is enhanced.

The invention provides a ship six-degree-of-freedom motion sensing and predicting method and device and a storage medium, and belongs to the technical field of ship motion sensing. Establishing a first measurement equation based on the rotation quaternion and the measurement value; based on the first system state change equation and the first measurement equation, the rotation angular velocity and the linear acceleration of the ship are obtained; establishing a six-degree-of-freedom state-space equation based on the rotation angular velocity and the linear acceleration within the preset duration; based on the rotation angular velocity and the linear acceleration of the ship, the optimal estimation of the system state is determined by adopting an extended Kalman filtering algorithm and a state-space equation, and the six-degree-of-freedom motion response condition of the ship is determined; and according to the six-degree-of-freedom motion response condition of the ship and the six-degree-of-freedom second system state change equation, predicting the six-degree-of-freedom motion change within the specified time. According to the method, the six-degree-of-freedom motion of the ship can be accurately estimated, and short-term six-degree-of-freedom motion of the ship can be predicted.

The invention discloses a multi-station multi-target angle correlation positioning method, which comprises the following steps of: classifying angle measurement sets from different aircrafts to obtain a plurality of groups of angle measurement value sets of different aircrafts possibly from the same target, and providing a reliable measurement set for a coupling weight least square angle positioning algorithm; secondly, establishing a group of overdetermined nonlinear angle measurement equations including position estimation errors and angle measurement errors of the aircraft, and proposing a coupling error weight least square angle positioning algorithm; and finally, each target angle measurement set obtained through classification by using an angle correlation screening algorithm is used as input of a coupling error weight least square angle positioning algorithm, a multi-target position estimation result is obtained, and a multi-target positioning process is completed. According to the method, the calculation complexity of the angle measurement set association process in the multi-target angle positioning process can be greatly reduced, the measurement association operation time is effectively shortened, and the multi-target angle positioning real-time performance and the positioning precision are improved.

The invention belongs to the field of navigation, and discloses a KF and PINN deep fusion-based adaptive state estimation method, which comprises the following steps of: establishing a discrete time state equation and a measurement equation of Kalman filtering, and setting initial conditions of a filter; constructing a neural network model based on KF and PINN; training a neural network model; utilizing the trained neural network model to predict statistical characteristics of noise to obtain a noise covariance matrix at the moment k; constructing a filtering time updating process; constructing a filtering measurement updating process; and repeating the steps to obtain all state posteriori estimations. According to the method, the problem of estimation errors caused by inaccurate noise covariance of traditional Kalman filtering is effectively solved, and the precision and robustness of state estimation are remarkably improved.

The invention provides a polarization / inertia / vision intelligent navigation method based on model error learning, which belongs to the field of navigation and comprises the following steps: modeling a polarization / inertia / vision integrated navigation system, and establishing a system state equation by taking an inertial navigation error and a camera pose error as system state quantities; establishing a system measurement equation based on the information of the polarization sensor and the visual sensor; a neural network learning modeling error is constructed by considering uncertainty modeling errors of a system state equation and a measurement equation caused by system parameter errors and environmental interference, and the precision of a polarization / inertia / vision integrated navigation system model is improved; the multi-state constraint Kalman filtering method of the embedded neural network is established for solving the problems that in an actual environment, sensor noise statistical characteristics are unknown and time-varying due to environmental factors. According to the invention, the navigation precision of the polarization / inertia / vision integrated navigation system in a complex environment can be improved.

An underwater tightly-coupled navigation method for SINS / DVL / USBL based on centralized filtering, the method comprising the following steps: (1) Select the state variables of the SINS / DVL / USBL navigation algorithm and construct the state equation of centralized filtering; (2) Convert and calculate the original beam frequency shift information in the DVL coordinate system according to the carrier velocity information output by the SINS, and convert and calculate the angle measurement information and slant range information in the USBL acoustic array coordinate system according to the carrier position information and the position information of the transponder; (3) Construct the SINS / DVL tightly-coupled measurement equation based on frequency shift measurement and the SINS / USBL tightly-coupled measurement equation based on relative measurement information; (4) Design the DVL and USBL fault detection and processing mechanisms, and design to isolate the DVL and USBL fault data. The present invention provides an underwater navigation algorithm for SINS / DVL / USBL based on centralized filtering, which can effectively solve the accuracy loss caused by the failure of DVL or USBL data during the navigation and positioning process, and improve the underwater positioning accuracy.

The invention relates to a power distribution network state estimation method and system in the technical field of power distribution networks, and the method comprises the following steps: building a power distribution network state estimation model which comprises a state equation and a measurement equation; performing state prediction based on the state equation to obtain a state prediction value and an error covariance prediction matrix, and performing measurement prediction to obtain an error covariance measurement initial matrix and a cross covariance initial matrix; calculating a fading factor, and updating the error covariance prediction matrix by using the fading factor to obtain an error covariance prediction adjustment matrix; performing measurement prediction based on the error covariance prediction adjustment matrix to obtain an error covariance measurement update matrix and a cross covariance update matrix; according to the method, the state estimation is updated, and the observation noise covariance is updated based on the updating result, so that the problem that the estimation precision and robustness are remarkably reduced due to the fact that an existing power distribution network state estimation method cannot accurately model unknown noise and is difficult to effectively track the rapid change of a system is solved.

The invention discloses a road slope displacement monitoring and early warning system, and the system comprises a data collection module which continuously obtains the multi-source monitoring data of a road slope; the data processing module is used for carrying out noise filtering processing on the displacement data to obtain an absolute displacement measurement value and a relative displacement change measurement value, inputting the absolute displacement measurement value and the relative displacement change measurement value into a Kalman filtering algorithm, and carrying out weighted fusion by establishing a system state equation and a system measurement equation to generate a displacement fusion sequence; inputting the displacement fusion sequence, the rainfall data and the geological structure data into a long short-term memory network model to generate a slope displacement evolution trend; and the early warning module is used for calculating a dynamic threshold value matched with the current geological condition and the rainfall intensity according to the slope displacement evolution trend, and generating and issuing graded early warning information when the predicted value of the displacement fusion sequence exceeds the dynamic threshold value. According to the invention, the accuracy, real-time performance and reliability of road landslide disaster early warning can be improved, the false alarm and missing alarm rate can be reduced, and the urgent demand of road safety operation for emergency disposal timeliness can be met.

The invention particularly relates to an inertial navigation / radar / satellite navigation three-combination emitter integrated navigation method based on Lie group, which comprises the following steps: responding to a navigation control instruction, and configuring a navigation system of an aircraft to enter an integrated navigation state; constructing a special European group based on the attitude features, the speed features and the position features, and establishing a motion description model based on the description of the special European group; acquiring navigation information of an inertial navigation system, and resolving the navigation information by using an error model under a special European group to obtain a state matrix; obtaining current measurement information; wherein the measurement information comprises satellite navigation subsystem measurement information and / or radar navigation subsystem measurement information; based on the measurement information, establishing an observation vector and a measurement equation of a corresponding subsystem under a special European group; resolving the state matrix, the measurement equation and the observation vector by using a Kalman filter to obtain navigation error estimation data; and performing feedback correction on the navigation error based on the navigation error estimation data.

The invention relates to the technical field of power system relay protection, in particular to an alternating-current transmission line state estimation fault phase selection method, system and device and a medium, and the method comprises the steps: mapping mutually coupled three-phase voltage and current components in a three-phase alternating-current transmission line from a phase domain to a mode domain space; mode domain voltage and current components of the new energy side and the power grid side are obtained; constructing a system measurement equation based on the mode domain voltage and current components to obtain an optimal estimation value; calculating a residual normalized sum of squares according to the optimal estimation value, and screening out an intra-region fault line by comparing the residual normalized sum of squares with a chi-square distribution threshold value; and performing secondary decoupling on the same fault current data of the fault line in the area, and determining the fault phase according to the residual normalized quadratic sum ratio of each mode domain component under the multi-mode phase-mode transformation combination. Through the state estimation method combining phase-mode transformation and the Bergeron model, rapid and accurate phase selection of the AC power transmission line fault is realized, and the fault identification precision is effectively improved.

The invention discloses a self-adaptive vehicle navigation filtering method and device, and belongs to the technical field of intelligent driving. The method comprises the following steps: constructing a nonlinear uncertainty tracking system model according to the mutability of an actual vehicle tracking state, the nonlinearity of a sensor measurement equation and the non-Gaussian property of a measurement error; aiming at a nonlinear uncertainty tracking system model, combining strong tracking filtering and a maximum entropy criterion, and constructing a cost function for estimating the optimal state of the unmanned vehicle; determining a fading factor in the cost function based on the orthogonality of the measurement residual sequence; constructing a novel adaptive navigation filtering algorithm by combining an unscented Kalman filtering framework according to the cost function and the fading factor; and carrying out data processing on the unmanned vehicle tracking system according to the constructed adaptive navigation filtering algorithm. According to the method, the problems of mutability, nonlinearity, non-Gaussian property and the like in a nonlinear tracking system can be inhibited at the same time, and the tracking precision and reliability of the unmanned vehicle in a complex environment are improved.

The invention relates to a multi-source intelligent combined pose measurement method for a floating hydrophone array, and the method comprises the following steps: 1, obtaining the pose information of each node of the hydrophone array through a multi-source sensing module, and synchronously receiving sound signals and extracting the TDOA information of each node under the condition that the sound source position is known; step 2, constructing a measurement equation based on TDOA information, and performing inverse solution on the position of each node of the hydrophone array in combination with a hyperbolic positioning principle and a sound velocity model; according to the method, an inverse solution positioning mode of'known sound source position and inverse solution of receiving array node position 'is adopted, a flexible underwater acoustic array dynamic measurement scene under the traction of a floating platform is specially aimed at, the technical limitation that traditional TDOA and USBL positioning depends on a fixed geometric structure is broken through, the method does not need to arrange a plurality of reference receiver arrays, and the positioning accuracy is improved. And a high-precision synchronous clock system is not needed, and high-precision acoustic positioning can be realized only by depending on a single sound source and the distributed nodes, so that the system complexity and the field deployment cost are remarkably reduced.

The invention discloses a structure parameter-load input joint identification method based on a Gaussian potential force model, and the method comprises the steps: obtaining a mass matrix, a stiffness matrix and a damping matrix, and constructing a state-space equation; modeling unknown load input into a zero-mean stationary Gaussian process with a specific covariance function to fuse prior information of the covariance function, and converting into an equivalent state-space equation; obtaining sparse dynamic response characteristics, and establishing a state space measurement equation; a structure state and load input are augmented into a state equation, the state equation and a measurement equation set are synthesized into a complete Gaussian process potential force model, unknown structure parameters are regarded as hyper-parameters, kernel function hyper-parameter optimization and structure parameter identification are achieved with the criterion of a maximized negative likelihood function, sequence reasoning is conducted on the Gaussian process potential force model, and the Gaussian process potential force model is obtained. Estimation of load input is obtained. The method has the advantages of being stable in parameter recognition result, high in precision, stable and reliable in load estimation, higher in robustness to model errors and measurement noise and the like.

The invention discloses a relative navigation method and system suitable for measurement asynchronization and communication delay processing, and relates to the field of navigation. The method solves the problems of measurement asynchronization, communication delay and the like of an existing measurement sharing relative navigation method in practical application. The method comprises the following steps: establishing a total error state equation of a relative navigation system for nodes in a cluster; establishing a multi-source measurement equation of a relative navigation system for the nodes in the cluster; based on a time division multiple access in-turn broadcast type communication mechanism, information data packets are shared by each node in the cluster; constructing a time updating equation and a measurement updating equation, and performing filter time updating and measurement updating; carrying out inertial navigation position extrapolation aiming at measurement asynchronization and communication delay to realize accurate alignment of measurement information containing lagging and an inertial navigation position in time; correcting the error feedback and outputting a relative navigation result. The method is also suitable for the application fields of relative navigation algorithms for measuring asynchronization and communication delay processing and the like.

The application discloses a power distribution network prediction auxiliary state estimation method and system based on an improved iteration process, and the method comprises the following steps: establishing a nonlinear discrete time state space model; generating a group of Sigma points through an unscented transformation, and substituting the Sigma points into a state transition equation to calculate a state prediction value and a state prediction error covariance matrix at a current time; substituting the Sigma points into a measurement equation to obtain a measurement prediction value, and calculating a Kalman gain; calculating a process noise covariance matrix at a next time; and correcting the state prediction value and the state prediction error covariance matrix at the current time to obtain an optimal state estimation value and an error covariance matrix thereof. The application avoids the risk of losing semi-positive definiteness of the covariance matrix in the iteration process from the algorithm mechanism, significantly enhances the numerical stability and robustness of the UKF algorithm, and ensures reliable application in high-dimensional complex power distribution network state estimation.

This invention relates to the technical field of positioning methods, specifically to a real-time positioning method for forest environments. The method includes the following steps: S1: Solving IMU and BeiDou data; S2: Establishing the BeiDou measurement equation and the IMU state equation; S3: Using ESKF to achieve data fusion and output compensation; S4: SLSTM network training and data augmentation; S5: After BeiDou loss of lock, the SLSTM network performs real-time prediction; after BeiDou recovery, step S3 continues. This method, through data augmentation technology, improves the robustness and adaptability of the neural network model, enabling the system to maintain high-precision positioning even in complex environments.