191 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 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 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 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 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.

The application discloses a road friction estimation method and device combined with vehicle platoon and extended Kalman filter, and the method comprises the following steps: designing state equation and measurement equation of road friction estimation; all the vehicles in the vehicle platoon except the tail vehicle send the estimation information of the road adhesion coefficient to the following vehicle through vehicle-to-vehicle communication; all the vehicles in the vehicle platoon except the head vehicle determine whether to use the estimation information of the front vehicle according to the estimation information of the road adhesion coefficient; all the vehicles in the vehicle platoon estimate the current road adhesion coefficient by using the extended Kalman filter, and the estimation process is to solve the state equation and the measurement equation based on the estimation information of the road adhesion coefficient at the last moment. The application designs the extended Kalman filter with a judgment module, and the extended Kalman filters of the platoon members are combined through vehicle-to-vehicle communication of the vehicle platoon, so that the estimation speed and precision of the following vehicle for the road adhesion coefficient are improved.

The invention relates to the technical field of target tracking, in particular to a target tracking algorithm fusing multi-coordinate system filtering results, which can be applied to the fields of radar detection, threat target early warning, auxiliary driving and the like. According to the method, firstly, system parameters and a tracking model are set, then, a Jacobian matrix of an EKF measurement equation is calculated, then, initialization of a CMKF filter and CMKF are carried out, then EKF initialization and EKF are carried out, and finally, tracking results of the EKF in a polar coordinate system and the CMKF in a rectangular coordinate system are fused to obtain a target tracking result. The method combines the EKF and CMKF filtering results to obtain the filtering result fused with multiple coordinate systems, not only does not introduce new parameters, but also improves the tracking precision of the target, has a small operand, and is convenient for engineering realization.

This invention discloses a method for monitoring the integrity of an inertial / radar system for shipboard landing. Based on the presence or absence of a fault, the total integrity risk is divided into fault-free integrity risk and integrity risk based on a fault detection mode. According to integrity index requirements, the probability of misleading information under each risk type is set. A filtered state equation model is established, and a measurement equation model is further set. Based on the measurements output from the measurement equation model and combined with the probability of misleading information under each risk type, the integrity level protection threshold (HPL) under fault-free conditions is calculated. FF Based on the measurements output from the measurement equation model, and combined with the probability of misleading information under each risk type, the integrity level protection threshold (HPL) under fault detection mode is calculated. FD According to HPL FF HPL FD The integrity protection level of the inertial / satellite combined output parameters is calculated. In the precision ship landing phase, where the accident rate is highest, the system can output the horizontal protection threshold under this scheme based on the confidence index required for the scenario.

The invention discloses a meta-heuristic adaptive robust azimuth gyro drift calibration method, and provides a technical scheme for improving the azimuth gyro drift calibration precision on a software level, and the method comprises the following steps: establishing a Kalman filtering model of an augmented measurement equation based on an angular rate; in combination with a variational Bayes-based measurement noise variance matrix adaptive estimation method and an improved sine and cosine heuristic multi-correction coefficient optimization algorithm, drift calibration of multi-channel measurement error decoupling and multi-channel error correction is realized, so that the drift calibration of multi-channel measurement error decoupling and multi-channel error correction can be realized while hardware cost and system complexity are not increased. Therefore, the method is very suitable for large-scale application and popularization.

A measurement pattern for monitoring overlay displacement of bonded wafers, the bonded wafers including a top wafer pattern and a bottom wafer pattern. The top wafer pattern includes a first portion having a width Wx1 measured along a first axis. The bottom wafer pattern includes a first feature having a width Wx2 measured along the first axis, wherein the first portion of the top wafer pattern is spaced apart from the first feature of the bottom wafer pattern by a target distance Dx, and wherein the measurement pattern satisfies the following measurement equations: Tx > Dx - Sx; Tx < Dx - Sx + Wx2; Tx > Sx; Tx < Sx + Wx1; and Tx < Dx + Wx1 + Wx2.

The application discloses an infectious disease trend prediction device and a use method thereof, and belongs to the field of infectious disease prediction.The device comprises a data receiving module, a data storage module, a state equation construction module, a measurement equation construction module and a prediction output module.The data receiving module is used for receiving infectious disease historical data from a server.The data storage module is used for storing the received infectious disease historical data and an infectious disease trend prediction value.The state equation construction module is used for constructing a state equation according to the infectious disease historical data, and inferring the state at the moment through the state at the last moment.The measurement equation construction module is used for constructing a measurement equation according to the infectious disease historical data, and fusing the value of the state equation.The prediction output module is used for combining the state equation and the measurement equation, and outputting an infectious disease time prediction value.

The application provides a space-based space target orbit determination method considering observation platform position error. The method respectively establishes observation platform position error models according to different orbit determination means of the space-based observation platform and provides a space-based space target orbit determination algorithm. When the space-based observation platform uses a global navigation satellite system (GNSS) for real-time positioning, the observation platform position error at each observation time is modeled as a Gaussian white noise sequence in a measurement equation, and an extended Kalman filter (EKF) is used for filtering estimation of the space target position and velocity. When the space-based observation platform is orbit-determined by using the tracking results of a ground station to inject values, the observation platform position and velocity error and the space target position and velocity are simultaneously used as state variables to be estimated between adjacent injection time points, an EKF is used for filtering estimation, and the filter is reinitialized at each injection time point, so that high-precision orbit determination of the space-based space target is realized.

The present invention relates to the field of combined navigation technology, and provides a shipborne inertial and starlight integrated calibration method and equipment based on external velocity damping. The method comprises obtaining a horizontal damping network of an inertial navigation system; obtaining the inertial navigation output latitude and inertial navigation output longitude of the inertial navigation system based on the horizontal damping network; selecting a horizontal damping parameter, establishing a state equation and a measurement equation of the star-inertial system under horizontal damping conditions; obtaining a Kalman filter based on the state equation and the measurement equation; obtaining a ship maneuvering condition through inertial navigation information, and when the ship maneuvering condition is met, performing integrated calibration of the inertial navigation system through the star-inertial combined system, and completing error estimation and compensation through the Kalman filter. The present invention compensates for the dynamic attitude measurement error of the inertial navigation by designing a Kalman filter with horizontal velocity damping, assisting star-sensitive equipment in achieving high-precision positioning, completing the integrated calibration of the star-inertial combined system, and maintaining the navigation accuracy of the inertial navigation system for a long time.

The invention discloses a combined system error correction and target state estimation method based on multi-angle measurement, which is applied to a three-dimensional passive multi-sensor multi-target tracking system. Comprising the following steps: constructing an augmented state vector based on a target state vector, an angle measurement deviation vector, a time deviation vector and a sensor position vector, and constructing an augmented state transition equation; performing modeling by taking azimuth angle measurement, pitch angle measurement and sensor position measurement as functions of an augmented state vector, constructing an augmented measurement vector and an augmented measurement equation, and determining a nonlinear relationship between the constructed augmented measurement vector and augmented state vector; azimuth angle measurement, pitch angle measurement and sensor position measurement are obtained through actual measurement, and an augmented state vector is estimated through linearization of a non-linear relation between a constructed augmented measurement vector and an augmented state vector and sequential filtering. The method can be applied to multi-source information fusion under a passive multi-sensor system, and the tracking and positioning precision of the target is improved.

The invention provides a Doppler radar error calibration method based on satellite navigation information assistance. The method comprises the following steps: step 1, constructing a sensor error model; step 2, establishing an alignment state equation between SINS / GNSS / Doppler radar travel; step 3, establishing an alignment measurement equation between SINS / GNSS / Doppler radar travel; 4, discretizing the state equation established in the step 2 by adopting a discretization method; and step 5, performing alignment filtering calculation during SINS / GNSS / Doppler radar travel by using a discrete Kalman filtering algorithm, and calibrating the Doppler radar error according to a calculation result. According to the method, initial alignment between marching is completed by using the GNSS and the Doppler radar auxiliary inertial measurement unit at the same time, the reliability and adaptability of the system can be improved, SINS initial alignment can be completed in the marching process, and meanwhile, Doppler radar error calibration compensation is performed in real time; therefore, the initial alignment can still be realized under the condition that the satellite signal is shielded.

The invention relates to the technical field of signal processing, and provides a broadband weak target passive direction finding method based on a single vector hydrophone strong interference condition, and the method comprises the steps: S1, collecting a radiation signal of a weak target through a single vector hydrophone; s2, preprocessing the radiation signal to obtain an azimuth signal of the weak target at the current moment; s3, setting an azimuth threshold interval at the next moment, screening frequency point signals of which the estimated azimuth falls in the set threshold interval at the next moment, and retaining frequency point signals dominated by the weak target; and S4, updating Kalman filtering by constructing a constant-speed linear model, a measurement equation and a prediction equation, and correcting the azimuth estimation result of the current frequency point signal by adopting the updated Kalman filtering to reduce the current low signal-to-noise ratio snapshot estimation error. According to the method, the influence of strong interference in the non-target direction is eliminated by using the azimuth interval, and the Kalman filtering is introduced to reduce the direction-finding fluctuation error of a low-signal-to-noise-ratio signal, so that accurate passive positioning of a weak broadband target under strong interference is realized.

The invention relates to a polarization / inertial navigation combined navigation method with an embedded unmanned aerial vehicle (UAV) dynamics model. The method comprises the following steps: taking the three-dimensional attitude error angle, velocity error, position error, angular velocity error and thrust coefficient error of the UAV dynamics model as state quantities and expanding them into inertial navigation state parameters to form system state quantities, and establishing a system state equation; taking the heading angle solved by a polarization sensor and the attitude, velocity and position of the UAV dynamics model as quantity measurements to establish a system measurement equation; establishing a credibility discriminant function of the polarization heading vector measurement based on heading angle innovation information, and designing a combined navigation mode switching and thrust coefficient error feedback strategy; and using a Kalman filtering method to estimate the system state quantities, namely the inertial navigation attitude misalignment angle, velocity error and position error, and the attitude error angle, velocity error, position error and thrust coefficient error of the UAV dynamics model, to achieve correction of the UAV attitude and position.

The application discloses a kind of star group between heterogeneous measurement data association and high-precision fusion orbit determination method, belong to space relative navigation technical field.The method includes grouping to the member star in star group, constructs the measurement set of the angle measurement and ranging of all member stars in time t, then calculates the position assumption set of member star, calculates the distance assumption value between member stars under each association assumption, constructs association degree evaluation index by distance assumption value and actual distance measurement, constructs cost matrix according to association degree evaluation index, and then obtains optimal association result, obtains the ranging and angle measurement data corresponding to all member stars at current time relative orbit determination, including using optimal estimation method to carry out state estimation, and including determining system state, constructing system measurement equation and system equation;Finally, the above steps are repeatedly executed for each measurement time, until filter convergence, obtain the high-precision orbit determination result of member star.

The invention relates to the technical field of inertial navigation and multi-source sensor fusion, and discloses a rental vehicle real-time positioning method and system based on quantum localization, and the method comprises the steps: a vehicle-mounted calculation unit synchronously collects first angular rate data of a vehicle-mounted micro-electromechanical inertial measurement unit and second angular rate data of a vehicle-mounted chip-level atomic spin gyroscope; performing a strapdown inertial solution using the first angular rate data; establishing a sliding integral window to execute numerical accumulation and downsampling operation on the first angular rate data, and generating an equivalent reconstruction angular rate which is physically isomorphic with the second angular rate data; according to the method, through a time domain integral matching mechanism, dynamic response phase lag errors caused by bandwidth differences of heterogeneous sensors are eliminated, and filtering divergence in a vehicle high-dynamic maneuvering scene is inhibited.