331 results about "Measurement equations" patented technology

The invention provides a bionic polarization semantic SLAM (Simultaneous Localization and Mapping) method based on a neural radiation field. A system comprises three parts, namely a bionic polarization pose estimation module based on factor graph optimization, a dense mapping module based on the neural radiation field and a semantic information positioning module based on bionic polarization infrared rays. The system firstly collects multi-sensor data, establishes a state equation and a measurement equation, and optimizes and estimates the pose by using a factor graph. Secondly, dense mapping is carried out by using the estimated pose and laser radar data and by adopting a neural implicit representation (NeRF-LOAM) model constructed by a laser radar odometer and a map through voxel embedding map generation and grid reconstruction. And finally, processing the polarization infrared fusion image through Yolov8 and SegNeXt, and realizing semantic positioning in combination with laser radar features. According to the system, the anti-interference performance and the environmental adaptability are remarkably enhanced, the mapping training time is shortened, and the system can be applied to multiple fields such as automatic driving, intelligent transportation and intelligent logistics.

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 racing car racing control method based on Gaussian process regression enhancement model predictive control, and belongs to the field of unmanned driving system trajectory planning and tracking control. The method mainly comprises the following steps: firstly, constructing a racing car racing random optimal control problem model comprising a state equation, a measurement equation and probability constraints, and secondly, respectively modeling system uncertainty and measurement links by adopting a Gaussian process regression model; the method comprises the following steps: firstly, designing prudent model predictive control of racing car racing and an extended Kalman filter GP-EKF based on Gaussian process regression to realize high-precision state estimation, finally, fusing the GP-EKF and the prudent model predictive control, constructing learning model predictive control GP-EKF-LMPC based on GP-EKF enhancement, and feeding back posterior state estimation to an optimization problem, so as to realize high-precision state estimation. And solving an optimal control instruction of racing car racing in real time. Experiments show that the method can significantly improve the control precision and robustness in a complex scene.

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 discloses an active power distribution network state estimation method and system based on GCN and Transform fusion. The method comprises the following steps: firstly, establishing a power distribution network graph model, and abstracting nodes in a power distribution network and lines connecting the nodes into a graph structure; secondly, constructing a photovoltaic power station physical model, and describing a relation between terminal voltage and current by adopting a five-parameter model; then constructing a state prediction equation based on Transform, and predicting a system state at a future moment by using historical measurement data; a GCN-based measurement equation is constructed, and relation modeling between the node state and the neighbor node state is realized through graph convolution operation; and finally, performing feature fusion on state prediction and measurement results by using a deep neural network, and outputting more accurate state estimation of the power distribution network. According to the method, the GCN is used for conversion of the measurement data, and spatial dependence between nodes and neighbors is fully mined; and the Transform is responsible for state prediction of long sequence time dependence, so that the prediction precision and robustness of the model are effectively improved.

The invention relates to a dynamic modeling method for an attitude control system under a dynamic fault. The method comprises the following steps: determining a modeling hypothesis of a nonlinear dynamic model; establishing a coordinate system used for modeling; rocket configuration parameters and engine related parameters are obtained; building displacement and acceleration descriptions of the three types of structures on the rocket; establishing a rigid body translation equation, a rigid body rotation equation, a rocket body elastic vibration equation, a propellant shaking equation and a sensitive element measurement equation; summarizing the five established equations to form a nonlinear dynamic equation set; and performing small-disturbance linearization expansion on the nonlinear dynamic equation set on the basis of the nominal reconstructed trajectory to obtain a linearized small-deviation equation set suitable for controller design and stability analysis. According to the method, the influence of the thrust drop fault is comprehensively considered, and the model can be used for carrying out relatively accurate dynamic response analysis and reconstruction scheme design under the fault.

The invention relates to an inertial navigation / polarization compass / laser velocimeter integrated navigation method, which belongs to the technical field of inertial navigation / integrated navigation, and comprises the following steps: mounting and fixing an inertial navigation system, a polarization compass and a laser velocimeter on a carrier, and acquiring coordinate systems of sensors; establishing a polarization compass error equation and a laser velocimeter error equation by considering a course angle installation error, a pitch angle installation error and a scale error of the laser velocimeter; and deriving an inertial navigation / polarization compass / laser velocimeter combined system state equation based on the error equation, establishing a measurement equation by taking the difference between the speed calculated by inertial navigation and the speed of the laser velocimeter and the product of the polarization vector of the polarization compass and the sun vector as observed quantities, and carrying out estimation and feedback correction on the system state. The invention provides an inertial navigation / polarization compass / laser velocimeter combined navigation method in a satellite navigation denial environment, which realizes full-autonomous and high-precision directional positioning.

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.

The invention discloses a mobile robot rolling time domain estimation method fused with threshold filtering. The method comprises the following steps: 1) establishing a mobile robot system model; 2) designing a measurement equation; 3) processing measurement abnormal data; 4) establishing a cost function; 5) establishing constraint conditions; and 6) designing a rolling time domain estimator. According to the method, a self-adaptive scale factor s is introduced to correct measured abnormal data, then rolling time domain estimation is carried out, and an optimization problem is solved, so that the influence of external interference on state estimation in the moving process of the mobile robot trolley is effectively reduced, and the combined positioning capability of a nonlinear system is further improved.

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 invention provides a power battery SOC estimation method and system based on a characteristic model, and relates to the technical field of SOC estimation, and the method specifically comprises the steps: obtaining three characteristic parameters of an open-circuit voltage, an internal resistance and a polarization voltage of a battery, introducing a temperature and aging correction mechanism, building a relation curve between the characteristic parameters and the SOC, and calculating the SOC of the battery according to the relation curve; mapping the characteristic parameters into SOC values through a fitting formula; constructing an SOC fusion model based on a relation formula of the calibrated characteristic parameters and the SOC, synthesizing SOC estimated values of the characteristics into a comprehensive estimated value by using a weighted linear combination method, dynamically adjusting weights of the characteristics through a particle swarm optimization algorithm, and determining an optimal weight coefficient; through the extended Kalman filtering, the comprehensive estimation value output by the model is corrected in real time, the SOC change is predicted based on the state transition equation in the correction process, the deviation between the estimation value and the true value is updated in combination with the measurement equation, and the real-time performance and reliability of SOC estimation are improved.

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 invention discloses a master-slave USV collaborative navigation method based on corner net condition adjustment, and belongs to the technical field of autonomous underwater navigation, and the method comprises the following steps: using a strapdown inertial navigation system to obtain a navigation state of a USV to be positioned at an initial moment, receiving a current position shared by other USV data in a USV formation by the USV to be positioned, converting the current position into a two-dimensional coordinate, and carrying out navigation on the two-dimensional coordinate; the USV to be positioned obtains the relative distance measurement information # imgabs0 # and the relative azimuth angle # imgabs1 # between the USV to be positioned and other USVs according to the equipped sensor, establishes a state equation and a measurement equation for describing the USV to be positioned, establishes a corner net condition adjustment mathematical model, processes measurement vectors, obtains a measurement vector adjustment value and a variance matrix thereof, sets an observation noise variance matrix, and obtains the measurement vector adjustment value of the USV to be positioned according to the measurement information # imgabs0 # and the relative azimuth angle # imgabs1 # of the USV to be positioned. And measurement updating is carried out in combination with DVL speed measurement information. By adopting the corner net condition adjustment-based master-slave USV collaborative navigation method disclosed by the invention, high-precision estimation and collaborative correction of the navigation state of the USV to be positioned can be realized.

The invention relates to a polarization / inertial navigation integrated navigation method of an embedded unmanned aerial vehicle kinetic model, which comprises the following steps of: expanding a three-dimensional attitude error angle, a speed error, a position error, an angular speed error and a thrust coefficient error of the unmanned aerial vehicle kinetic model into inertial navigation state parameters as state quantities to form system state quantities; establishing a system state equation; taking the course angle calculated by the polarization sensor and the attitude, the speed and the position of the unmanned aerial vehicle kinetic model as quantity measurement, and establishing a system measurement equation; establishing a credibility discrimination function of polarization course measurement based on course angle innovation, and designing a combined navigation mode switching and thrust coefficient error feedback strategy; a Kalman filtering method is used for estimating system state quantities, namely an inertial navigation attitude misalignment angle, a speed error and a position error, and an attitude error angle, a speed error, a position error and a thrust coefficient error of an unmanned aerial vehicle dynamic model, so that correction of the attitude and the position of the unmanned aerial vehicle is realized.

The invention relates to a flight calibration method and system for structural parameters of a star sensor and an inertial unit, and the method comprises the following steps: setting parameters and environmental parameters of the star sensor and the inertial unit, and setting an initial state of a structural parameter deviation estimator; after the inertial unit and the star sensor output data, performing time propagation on estimation values and estimation covariance of constant zero offset, zero offset drift and structural parameter deviation; calculating attitude increments of the inertial unit and the star sensor by using the angular velocity and star observation information, and constructing a measurement equation of structural parameter deviation; solving a corresponding regularization least square problem by using a regularization robust filtering framework, and completing estimation value updating and estimation covariance updating of constant value zero offset, zero offset drift and structure parameter deviation; and after the stable estimated value of the structure parameter deviation is obtained, the structure parameters are compensated. According to the method, high-precision structural parameter calibration of the star sensor and the inertial unit can be realized, and complicated astronomical-ground coordinate conversion can be carried out without the aid of time service positioning information.

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 relates to a real-time azimuth tracking model-based algorithm based on a horizontal circular array. The real-time azimuth tracking model-based algorithm comprises the following steps: S1, determining equipment parameters and water area environment parameters for measuring a moving target; s2, constructing an azimuth angle state space equation and a calculation formula of a sensor model; s3, measuring the background noise level of the water area, and giving a noise coefficient of a measurement equation; s4, setting an azimuth angle initial value by observing the approximate direction of the moving target by a measurer, or estimating the azimuth angle initial value through a conventional beam forming method; and S5, performing calculation through an unscented Kalman filter azimuth tracking algorithm framework, and solving the azimuth angle of the moving target in real time. According to the unscented Kalman filtering framework, the input is sound pressure real-time data, the output is a moving target azimuth angle, the online real-time performance of operation is ensured, meanwhile, a measurement equation based on a plane wave propagation model is constructed according to the spatial position of a horizontal circular array, and by means of the isotropic property of the circular array, the real-time performance of the horizontal circular array is improved. And the reliability of estimation results of the algorithm in all directions is ensured.

The invention relates to the technical field of lithium ion battery state estimation, and discloses an ASRUKF-based multi-point internal temperature online estimation method for a full-life wide-temperature square lithium battery. According to the method, a lithium battery electrothermal coupling model is constructed based on a second-order RC equivalent circuit model and a three-heat-source heat production model in combination with a two-state lumped heat transfer model; model parameters are identified, so that precise modeling of internal temperature estimation of the lithium ion battery under the full-life wide temperature is realized; establishing a multi-point temperature heat transfer model, discretizing representative region temperature to obtain a system state space equation and a measurement equation, and realizing multi-point temperature estimation of the square battery; and the internal temperatures of different points of the square lithium battery are estimated by fusing an adaptive square root unscented Kalman filtering algorithm, and the estimated internal temperatures are used as feedback quantity to update circuit model parameters, so that coupling of an electric heating model and online estimation of the internal temperatures of multiple points are realized.

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 discloses a data chain relative navigation information fusion method and device, and the method comprises the steps: firstly building a relative navigation information fusion state equation according to input information, and then building a relative navigation information fusion measurement equation according to the input information to guarantee a relative space-time reference under a satellite navigation denial condition; based on the relative navigation information fusion state equation and the relative navigation information fusion measurement equation, selecting a corresponding Kalman filter according to the measurement information to carry out relative navigation information fusion so as to obtain a navigation result, and correcting a navigation error state according to the measurement information after a round of state estimation of relative navigation information fusion is completed. And the corrected relative positioning result is reported to the airborne avionics system. Different types of measurement information are fused, and the information is optimally fused by using a Kalman filter, so that the precision and reliability of relative positioning are improved, the measurement information is quickly processed in a real-time environment, and the robustness and stability of the system are kept by correcting a navigation error state.

The invention discloses a transfer alignment method and device suitable for long endurance, and relates to the technical field of navigation.The method comprises the following steps that navigation of a sub inertial navigation system is initialized based on position information, speed information and attitude information provided by a main inertial navigation system; carrying out inertial navigation on the basis of the apparent velocity increment and the angular increment of the sub inertial navigation system subjected to navigation initialization; establishing a state equation of a kalman filter, and performing time updating; establishing a measurement equation of a kalman filter, and utilizing speed information and attitude information provided by a main inertial navigation system to construct measurement and update the measurement; and after time updating and measurement updating are completed each time, the error covariance matrix P is constrained. According to the method, the error covariance matrix P is kept in symmetry and non-negative nature for a long time, so that the time requirement of rapid alignment is met, and the precision requirement of long-time alignment can also be met.

The invention provides a method for accurately estimating target geometric features under laser radar point cloud integration. The method is divided into a modeling stage and an operation stage. In the modeling stage, a system state equation and a measurement equation are established according to the relation between the real geometrical characteristics and motion characteristics of a target during point cloud integration, the relation between the geometrical characteristics detected by a sensor and a sampling period and the relation between point cloud integration times. In the operation stage, firstly, original point cloud is preprocessed, a non-ground point cloud set is segmented, and then point cloud integration and clustering are performed to realize target detection, so that a target measurement set is obtained; secondly, carrying out one-step prediction, calculating target similarity, and carrying out target and trajectory association by utilizing the similarity; and finally, the optimal estimation of the geometric features and the motion features of the target is obtained by using Kalman filtering. According to the method, the precision of target geometric feature estimation under the point cloud integration method can be improved, the small target detection performance of the low-line-number laser radar is enhanced, and the cost of a target detection system based on the laser radar is reduced.