107 results about "Measurement equations" patented technology

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 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 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 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 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.

The present application belongs to the field of machine tool error precision measurement, and discloses a machine tool geometric error detection device and a rapid measurement method. In view of the problems of existing instruments and methods, such as multiple installations, low efficiency, and easy to be disturbed by environment, the machine tool geometric error detection supports multiple rod synchronous installation and rapid disassembly through the spindle end multi-interface spherical hinge tooling and the base end magnetic attraction structure, and realizes multiple error synchronous measurement at one time. In view of eccentricity and zero point error in assembly, the error parameter decoupling calibration method based on linearization measurement equation and least square is adopted to obtain the base station position and parameters. Further, through the multi-rod collaborative measurement and pseudo base station compensation mechanism, the machine tool positioning error, straightness error and perpendicularity error are solved synchronously in single closed trajectory operation. The present application realizes efficient and reliable evaluation of assembly error calibration compensation and machine tool space error, and is suitable for rapid measurement of numerical control machine tool geometric error.

The application discloses a polarization differential reference-based navigation orientation method and device, equipment and a medium, which are applied to a polarization differential reference station and a user station, and the user station is located within the effective radius of the reference station. The polarization angle non-perpendicular error of each pixel point observation area is taken as a state vector to be estimated, a state equation of a polarization angle non-perpendicular error time domain interference observer is established; a polarization vector is constructed based on a polarization angle measurement value; a measurement equation and a system model of the interference observer are established; a corresponding relationship between the polarization angle non-perpendicular error to be compensated and a sensor observation vector is established; the polarization angle non-perpendicular error value is determined according to the corresponding relationship and a real-time sensor observation vector obtained through a user station navigation system; the polarization angle is compensated, and polarization heading calculation is carried out by using the compensated polarization angle, so that real-time heading information of a carrier is obtained. According to the embodiment of the application, the navigation precision and environmental adaptability of the user end combined navigation system can be improved.

This invention relates to an adaptive, lightweight, online calibration method for extrinsic parameters of a vehicle-mounted integrated navigation system. The method includes: Step 1, augmenting the navigation system extrinsic parameters to a Kalman filter to construct a system error model; Step 2, obtaining the observability analysis results of the extrinsic parameters through observability matrix analysis; Step 3, grouping the extrinsic parameters according to the required maneuvering mode to achieve piecewise decoupled calibration and Kalman filter dimensionality reduction; Step 4, establishing corresponding state equations and measurement equations through the system error model, and then constructing an adaptive feedback factor; Step 5, using the adaptive feedback factor to reflect the accuracy of error estimation, determining whether to provide feedback to the extrinsic parameters and the magnitude of the feedback. This invention solves the technical problems in the prior art, such as the signal being easily affected by external factors, leading to reduced navigation accuracy, and the system extrinsic parameter calibration relying on the vehicle performing specific continuous maneuvers.

The application discloses an APU and a method for diagnosing a dynamic process sensor fault of a control system, and the method comprises the following steps: using a small disturbance method to establish a wide-range linear dynamic model of the APU; on the basis, using a Kalman filter cluster to design dynamic process sensor fault diagnosis isolation and reconstruction logic; judging a dynamic process, and then using a residual weighted least square method to obtain a sensor fault period; in the dynamic process, disconnecting the Kalman filter to isolate the fault, bringing the saved flow coefficient which has not entered the dynamic into a measurement equation, and using the same to reconstruct the sensor fault, and calculating to obtain a correct APU fault sensor value. The application provides a solution for the fault diagnosis isolation and reconstruction problem of the APU system in the dynamic process. Compared with the existing research, the application can also effectively eliminate the estimation error and ensure the accuracy of the flow coefficient estimation in view of the deviation caused by the dynamic process and the fault to the flow coefficient estimation.

The invention relates to the technical field of radars, and discloses a multi-target tracking method for a radar, and the method comprises the steps: obtaining the observation information of the radar for multiple targets; performing feature extraction according to the observation information to obtain the number of lower sampling points of each target and the position of the lower sampling points of each target; performing calculation according to the observation information, the number of the lower sampling points and the positions of the lower sampling points to obtain the state of the lower sampling point of each target and the weight of the lower sampling point of each target; according to the state of the lower sampling point and the weight of the lower sampling point, an extended Kalman filtering algorithm is adopted for calculation, and a state prediction value of each target is obtained; calculating according to the state prediction value to obtain a measurement equation; and according to the measurement equation and the state prediction value, performing multi-hypothesis tracking on each target to obtain a prediction state of each target. According to the method, the problem that a target tracking result is unstable in a complex environment or during long-time tracking can be solved.

The application discloses an advanced posterior Cramer-Rao lower bound considering target measurement uncertainty state correlation, comprising the following steps: step one, determining a target state equation of a target motion process and a measurement equation of a double-base radar when target tracking is performed; step two, after the target state equation and the sensor measurement equation are obtained, a Fisher information matrix of target state estimation of the target at time k is recorded as J k , and the lower bound of the target state estimation at time k is the inverse matrix of J k , that is, the advanced posterior Cramer-Rao lower bound; step three, calculating a conditional measurement information matrix; step four, substituting the calculated conditional measurement information matrix into the advanced posterior Cramer-Rao lower bound. The method focuses on the influence of target missed detection and measurement noise on the PCRLB under the influence of the target-radar geometric position under the basis of clutter interference. Simulation experiments verify that, compared with other PCRLB methods, the method of the patent can obtain a more accurate lower bound under the condition of highly uncertain double-base radar measurement information.

This invention discloses a transfer alignment method and device based on a left-invariant error model of Lie groups, belonging to the field of inertial navigation systems. The method first establishes a left-invariant error state model between the master and sub-inertial navigation systems based on Lie group theory; secondly, it constructs attitude and velocity error differential equations that simultaneously consider the effects of lever arm effects and installation error angles; then, it selects attitude and velocity measurements defined by the Lie group and establishes corresponding measurement equations; finally, it designs an unscented Kalman filter for the nonlinear state equations and measurement equations to jointly estimate parameters such as misalignment angle, velocity error, lever arm vector, installation error angle, and inertial navigation zero bias. The transfer alignment method proposed in this invention not only fully considers the actual lever arm errors between the master and sub-inertial navigation systems, improving the completeness of system error modeling, but also uses the local navigation coordinate system as the reference coordinate system, which is closer to actual engineering needs and has good versatility and engineering application prospects.

A multi-frame coherent detection and tracking method for weak target line spectra is disclosed, based on multi-frame coherent integration. Step 1: Perform multi-frame coherent integration on the passive sonar received data to obtain measured values; Step 2: Establish a state equation including a target presence indicator variable; Step 3: Establish a multi-frame coherent integration measurement equation based on the coherence of the signal phase in the multi-frame data; Step 4: Derive the likelihood ratio function based on multi-frame coherent integration that matches the measurement equation; Step 5: Combine the measured values ​​from Step 1 and the multi-frame coherent detection and tracking model established in Steps 2 to 4, and use a particle filter iterative algorithm to obtain a more robust target line spectrum detection and tracking result. This invention addresses the problem of numerous missed detections in single-frame detection and tracking methods caused by amplitude fluctuations in the received signal in dynamically time-varying marine acoustic channels. It can be applied to most sonar equipment.

The present application discloses a Kalman-filter-based adaptive microphone array noise reduction method and apparatus. The method includes: acquiring an input signal at each time instance; establishing a superdirective filter model and using it to filter the input signal thereby generating a first reference signal for each time instance; establishing a beamforming filter model and using it to filter the input signal thereby generating a second reference signal for each time instance; establishing a Kalman filter model as well as a process equation and a measurement equation for each time instance; generating a Kalman gain for each time instance based on errors corresponding to the process equation and measurement equation to allow the Kalman filter model, based on the Kalman gain, to eliminate the interfering noise from the first reference signal and the second reference signal and to generate a final output signal for each time instance.

The application discloses a warehouse scene mobile robot and sensor network node cooperative positioning method, which comprises the following steps: S1, arranging sensor network nodes, starting the robot, collecting robot motion information, robot and node distance measurement information and node distance measurement information, and filtering the relevant distance measurement information; S2, establishing a distance measurement correction model, and using the least square method to fit to obtain distance measurement correction parameters and initial values of position coordinates of unknown nodes in the sensor network; S3, establishing a state equation and a measurement equation of the robot-sensor network system; and S4, based on the derived system state equation and measurement equation, the overall state of the system is estimated based on the improved extended Kalman filter algorithm, and the simultaneous positioning of the mobile robot and the sensor network node in the warehouse scene is realized. The method can improve the distance measurement accuracy in a complex environment and the positioning accuracy of the robot and the sensor network node.

The application discloses a kind of polarization / sun-sensing sensor parameter online mutual calibration methods, belong to bionic polarization combined navigation technical field.The method core is, respectively using the polarization vector measured by polarization sensor and the sun vector measured by sun-sensing, constructs the cross-measurement equation for calibrating the parameter of the other party, realizes the mutual correction of the parameter of two sensors.To cope with complex environment, the application innovatively based on the Euclidean distance of polarization degree and the sun image coordinate and sun-sensing main point coordinate, designs the reliability discrimination function of polarization measurement and sun-sensing measurement, and accordingly formulates adaptive online mutual calibration switching strategy.Finally, the method of unscented Kalman filter is used to carry out online joint estimation to state quantity.The application can realize the parameter online mutual calibration of polarization sensor and sun-sensing without relying on high-precision turntable and other external reference, significantly improves the overall perception accuracy and long-term adaptability of sensor in time-varying environment.

The invention discloses a model data hybrid-driven polarization geomagnetic composite orientation method, which comprises the following steps of: after acquiring atmospheric polarization and carrier magnetic field information, calculating an initial course angle; defining a state equation based on a geomagnetic orientation method and an error model thereof, establishing a measurement equation in combination with polarization camera data, and preliminarily estimating a course angle of the composite orientation system by using a UKF (Unscented Kalman Filter); a bidirectional time sequence enhancement network model is introduced, time sequence features are extracted, through a bidirectional structure, multi-layer stacking and a nonlinear modeling mechanism, feature representation is optimized through a normalization layer, course angle errors estimated by the UKF are corrected, and an optimal solution of the course angle is obtained. According to the method, the UKF model and the BiTAN model are combined, the limitation problem that the UKF processes non-stationary time sequence data and burst noise in a dynamic time-varying environment is solved, the precision and stability of polarization geomagnetic composite orientation are effectively improved, and the orientation performance of a navigation system in satellite denial and complex environment scenes is ensured.

The invention discloses a time-varying gravity data probability principal component analysis method considering measurement error covariance. The method comprises the following steps: constructing a measurement equation; introducing a monthly scale and a non-diagonal measurement error covariance matrix provided by a data processing center, and setting a variance component unknown number to adjust the consistency of the variance component unknown number; performing three-decomposition on the total signal by adopting improved PPCA, and realizing parameter maximum likelihood estimation by combining an EM algorithm; selecting the number of principal components of the optimal signal in the alternative set through AIC; and outputting a strip-removed signal reconstruction result and a plurality of space-time orthogonal signal components thereof, and meanwhile, giving out estimation of noise and measurement errors. According to the method, measurement errors such as stripes can be effectively distinguished and suppressed while real geophysical signals are reserved, and the result quality is remarkably improved in the aspects of signal reconstruction and space-time analysis; and meanwhile, the method has the advantages of low calculation amount, high convergence speed and the like, and is suitable for processing GRACE / GFO data with inter-monthly covariance change characteristics.

The invention relates to a real-time water-turbine generator set frequency filtering method and system. The method comprises the steps that a frequency measurement value of a water-turbine generator set is calculated, and a state equation and a measurement equation are established for parameter prediction; updating a state estimation value in real time by adopting an improved Kalman filtering algorithm; calculating a measurement trust coefficient R value according to a concept of fusion variance of a measurement equation, and further calculating a frequency prediction value at the moment; calculating a standard deviation according to the state estimation value and the average value at the previous moment, performing nonlinear transformation on the standard deviation according to a state equation to obtain a prediction trust coefficient Q value, and calculating a state prediction value at the moment; when the Kalman gain tends to be smooth, calibrating a state estimation value; according to the method, on the premise that hardware is not modified, fusion filtering and noise covariance estimation are carried out on the collected data through the improved efficient Kalman filtering algorithm, the measurement precision is improved, and the purpose of measuring the real frequency value is achieved.

The invention discloses a power distribution network prediction auxiliary state estimation method and system based on iteration process improvement. The method comprises the following steps: establishing a nonlinear discrete time state space model; generating a group of Sigma points through unscented transformation, substituting the Sigma points into a state transition equation, and calculating a state prediction value and a state prediction error covariance matrix at the current moment; substituting the Sigma point into a measurement equation to obtain a measurement predicted value, and calculating a Kalman gain; calculating a process noise covariance matrix at the next moment; and correcting the state prediction value and the state prediction error covariance matrix at the current moment to obtain an optimal state estimation value and an error covariance matrix thereof. According to the method, the risk that the covariance matrix loses the semi-positive qualitative property in the iteration process is avoided from the algorithm mechanism, the numerical stability and robustness of the UKF algorithm are remarkably enhanced, and reliable application in high-dimensional complex power distribution network state estimation is ensured.

The present application relates to the technical field of inertial navigation, in particular to a single-axis rotation inertial navigation all-zero bias estimation method, device and medium, the method comprising the following steps: installing two sets of single-axis rotation inertial navigation systems on a carrier, the first single-axis rotation inertial navigation system is installed on a base, and the second single-axis rotation inertial navigation system is installed on a platform; after the inertial navigation system is started and stabilized, the two sets of single-axis rotation inertial navigation systems are rotated according to different rotation strategies, angular velocity and specific force information output by the two sets of single-axis rotation inertial navigation systems are collected, and the angular velocity and specific force information are demodulated; a measurement equation is constructed; a state equation is constructed; and all-zero biases of the two sets of single-axis rotation inertial navigation systems are estimated in real time through Kalman filtering algorithm according to the measurement equation and the state equation. The present application improves the autonomous navigation performance of single-axis rotation inertial navigation, and solves the problem that the existing single-axis rotation inertial navigation system is difficult to estimate and compensate the z-axis zero bias of inertial devices.

The application discloses a kind of self-adapting vehicle navigation filtering method and device, belong to intelligent driving technical field.It includes: according to the abruptness of actual vehicle tracking state, the nonlinearity of sensor measurement equation and the non-Gaussian of measurement error, construct nonlinear uncertainty tracking system model;For nonlinear uncertainty tracking system model, combined with strong tracking filter and maximum entropy criterion, construct the cost function of unmanned vehicle optimal state estimation;Based on the orthogonality of measurement residual sequence, determine the fading factor in cost function;According to cost function and fading factor, combined with unscented kalman filter framework, construct new self-adapting navigation filtering algorithm;According to the data processing of unmanned vehicle tracking system of the self-adapting navigation filtering algorithm constructed.This application can simultaneously suppress the abruptness, nonlinearity and non-Gaussian in nonlinear tracking system and other problems, improve the tracking precision and reliability of unmanned vehicle in complex environment.

The application discloses a multi-radar joint positioning method based on azimuth angle and Doppler velocity, and mainly solves the problems of poor positioning precision caused by large sensor measurement error and insufficient utilization of multi-source data. The method is aimed at a sea surface target moving at a constant speed, and uses target direction finding results of 1-2 fixed passive radars and target Doppler velocity measurement values of 1 fixed ground wave active radar. According to different numbers of radars, different filter initial values and covariance matrices are constructed, corresponding measurement equations and different filter starting times are determined, and filtering is performed on the target state according to the constant speed motion model. The filtering output is used as the positioning result of the target. The method uses the measurement of passive radars and ground wave radars of two different systems, reduces the influence of large single measurement error on the positioning result, shortens the positioning time, and improves the positioning precision.