40 results about "Total least squares" patented technology

The invention discloses a multi-beam installation deviation overall calibration method based on prior submarine topography, and belongs to the field of multi-beam data processing. The method mainly comprises the following steps: preprocessing multi-beam original data, performing point cloud registration to obtain matching point pairs, and resolving the installation deviation by total least squares. In submarine topography measurement operation of a multi-beam sounding system, after a transducer is disassembled and reassembled, installation deviation calibration needs to be carried out again, but an existing calibration method is complex in operation and low in efficiency. According to the method, the multi-beam installation deviation can be effectively calibrated, the operation process is simplified, the operation efficiency is improved, and the operation cost is reduced. A new solution is provided for the installation deviation calibration problem of the multi-beam sounding system, and the method has practical engineering application value.

The invention discloses a multi-view camera pose determination method, system and device, a medium and a product, and relates to the field of computer vision, and the method comprises the steps: constructing coordinate systems of a motion platform at different moments; determining transformation of each camera relative to a coordinate system and a direction vector and a distance vector of a Pluecker straight line corresponding to an image matching point; determining an initial essential matrix between the initial coordinate system and the transformed coordinate system based on a multi-camera pose solving linear method; determining an initial rotation matrix through the initial essential matrix; determining an initial translation vector based on a total least square method or a scaling factor recovery method; further determining an intermediate rotation matrix and an intermediate translation vector of each camera at different moments; finally, determining an optimized rotation matrix and an optimized translation vector between the initial coordinate system and the transformed coordinate system based on Simpson errors and an LM algorithm; according to the invention, the precision and robustness of determining the pose of the multi-view camera can be improved.

The invention discloses a robust robust sequence extraterrestrial celestial body terrain reference surface fitting method and system, and relates to the technical field of terrain reference surface fitting, and the method comprises the steps: collecting three-dimensional laser point cloud data to construct a reference plane geometric model, and building a joint error model of an observation value error and a design matrix error; adopting a sequence PEIV total least square framework to carry out grouping iteration adjustment on the point cloud data, and recursively updating parameter estimation by utilizing an error propagation matrix and a matrix inversion formula; introducing a two-factor robust mechanism, dynamically calculating a weight according to a residual error and estimating a robust scale; and circularly optimizing the parameters through an iterative strategy until a convergence condition is met, and outputting reference plane parameters. According to the method, different terrain complexity, data scales and error distribution scenes can be covered, the adaptability and engineering practicability of terrain datum plane fitting are remarkably improved, and reliable geometric reference is provided for subsequent obstacle detection and safe area decision making.

The present invention discloses a sparse-constrained total least log hyperbolic cosine adaptive filter, belonging to the field of digital filter design. This adaptive filter is established by linear constraint conditions and the log hyperbolic cosine function, and at the same time, an l1 norm penalty term is added to the cost function, and the total least squares method is introduced. By adding the l1 norm penalty, the problem of performance degradation of the algorithm in the case of unknown system sparsity is solved. The l1 norm-based constrained total least log hyperbolic cosine adaptive filter disclosed by the present invention can be applied to the identification of sparse systems and can also be used in adaptive beamforming to reduce energy consumption.

The invention provides a spatial positioning method for surveying and mapping, and relates to the technical field of laser radar positioning. The method comprises the following steps: projecting color grid light to a to-be-mapped area through color coding to form a positioning grid, acquiring two images for turning on and off the grid light by using image acquisition equipment, extracting grid intersection points as control points through color recognition, and obtaining pixel coordinates of the control points; measuring light is emitted to the control point through at least three measuring light projection points, and the three-dimensional coordinates of the control point are solved through spatial distance intersection; in combination with the pixel coordinates and the three-dimensional coordinates of the control points, reversely solving the pose of the image acquisition equipment through a heterovariance total least square model; the pixel offset of a target feature point and the pixel offset of an adjacent control point of the target feature point are recognized and converted into the actual distance offset in combination with the pose, and therefore the three-dimensional coordinates of the feature point are calculated. According to the invention, optical active projection and optical distance measurement are effectively fused, and high-efficiency three-dimensional space positioning is realized.

A method for error self-calibration and compensation of liquid floated gyro platform inertial navigation system is provided. In the condition of static base, the platform is locked at 13 frame angle positions, then the platform is controlled to track inertial coordinate system through the stable loop, and the measurement values of accelerometer and frame angle sensor are collected. The platform attitude angle in the earth coordinate system is calculated by using the platform attitude solution method of inertial navigation system, and is compared with the platform attitude angle measured by the platform frame angle sensor to obtain the attitude misalignment angle observation. The state misalignment angle sequence of each tracking inertial coordinate system process is linearly fitted to obtain the comprehensive angular velocity error of the inertial navigation system at each calibration position. The residual of the zero order, first order and cross term drift error coefficients is solved by using the total least squares algorithm, the estimated value of the measured parameter is corrected, and the self-calibration result of each drift error coefficient is obtained by iterative calculation until the iterative convergence criterion is met.

A total least squares recursive Lagrangian estimator for estimating parameters of a physical system (100) considered to be linear, such as a motor vehicle. An estimation device (110) comprises a measurement unit (114) configured to obtain successive input measurements Ak and successive output measurements yk of the linear system, and an estimation unit (116) configured to estimate one or more parameters xk recursively from the input and output measurements. A k+1 estimation recursion comprises the following operations: (1) (2) (3) (4) (5) optionally (5') (6) (7) where Qa,k and Qy,k are weighting matrices; δxk, and Pk are inter-recursion variables; is an instrumental variable correlated with the variables; Qz,k, ΔAk, Kk are internal variables; α is a regularization term and λ is a forgetting parameter. Figure for abstract: Fig 2

The invention relates to the technical field of power system monitoring, in particular to a higher harmonic and inter-harmonic measurement method and system based on an improved matrix bunch, and the method comprises the steps: firstly receiving a synchronous phasor data sequence of a power system, and constructing an L * P Hankel matrix Y; carrying out SVD (Singular Value Decomposition) on the Hankel matrix Y; constructing a matrix bundle based on a total least square framework, and solving a system matrix; and conjugate symmetry physical constraint is applied to a solution result, and oscillation modal parameters including frequency, damping ratio and amplitude are extracted, so that the inherent defects of low precision, limited resolution and incapability of analyzing attenuation dynamics when low-frequency higher harmonics, inter-harmonics and attenuation harmonics are measured in the prior art are solved, and the method is suitable for the measurement of the low-frequency higher harmonics, inter-harmonics and attenuation harmonics. And harsh requirements of a modern intelligent power grid on high-precision monitoring and analysis of the electric energy quality are difficult to meet.

A kind of double-axis frame inertial navigation system error self-calibration and compensation method, under static base condition, in 8 frame angle position locking platform, acquisition accelerometer measurement value, calculate the specific force measurement value of inertial navigation system in each locking position platform coordinate system, and calculate the attitude matrix of inertial navigation system in each locking position platform coordinate system to local horizontal coordinate system, obtain the acceleration of inertial navigation system in each locking position local horizontal coordinate system, as error observation quantity.Using total least squares algorithm to obtain the residual of error coefficient, correct the estimated value of measured parameter, carry out iterative calculation until meeting iteration convergence criterion, obtain the self-calibration result of each error coefficient;Through the self-calibration and compensation of error coefficient, the influence of shaft system non-orthogonal error on the attitude measurement precision of double-axis frame inertial navigation system carrier or base can be improved.

The present application relates to a robot kinematics calibration method based on truncated total least squares regularization, and a parallel robot is provided with a data acquisition system, including a moving platform, a base, a branch chain and a linear motor. The method comprises: obtaining the kinematics parameters of the parallel robot, inputting the inverse kinematics parameter error model, obtaining the conversion error between the coordinate systems and the mapping relationship between the kinematics parameter errors, obtaining the measurement data of the data acquisition system and the conversion relationship between the coordinate systems, making the measurement data uniform in the base coordinate system, identifying the parameter error through the truncated total least squares regularization method, optimizing the selection of the truncation factor, obtaining the best regularization parameter, and compensating according to the identified parameter error to update the controller kinematics parameters. The present application designs a complete low-cost and mature calibration scheme for most parallel robots, which can improve the kinematics calibration accuracy of the parallel robot.

The invention belongs to the technical field of visual inspection, and particularly discloses a large component measuring system based on a six-dimensional sensor and a calibration method of the large component measuring system. According to the measuring system, the laser tracker and the six-dimensional sensor serve as external tracking equipment, and compared with an external tracking device based on a laser target ball, the measuring system has the advantages of being high in measuring precision, high in measuring efficiency and the like. In addition, the measurement system obtains relative poses of two adjacent times of scanning of the scanner in a mode that sphere centers obtained by two adjacent times of measurement and calibration of a sphere array are rigidly matched, constructs a relational expression of calibration parameters to be solved and the relative poses, and solves the calibration parameters in the relational expression through a total least square method based on a Kronecker product; the calibration result of the measurement system calibration method is more accurate.

This application belongs to the field of visual inspection technology, specifically disclosing a large component measurement system and its calibration method based on a six-dimensional sensor. The measurement system of this application uses a laser tracker and a six-dimensional sensor as external tracking devices, which has advantages such as high measurement accuracy and high measurement efficiency compared to external tracking devices based on laser target spheres. Furthermore, the measurement system of this application obtains the relative pose of two adjacent scans of the scanner by rigidly matching the centers of the spheres obtained from two adjacent measurements of the calibration sphere array, and constructs a relationship between the calibration parameters to be solved and the relative pose. The calibration parameters in the relationship are solved using the total least squares method based on the Kronecker product. The calibration results of the calibration method of this application are more accurate.

The present invention provides a method and system for online compensation of vector magnetic interference of a rotary-wing UAV, belonging to the field of geomagnetic vector measurement technology. In order to solve the problem that the magnetic vector measurement value is affected by the magnetometer noise and attitude measurement noise at the same time, resulting in the accuracy of geomagnetic vector calculation being limited and the magnetic compensation effect being limited; and the problem that the maneuvering attitude of the rotary-wing UAV is limited and the multicollinearity of the measurement data is serious, resulting in difficulty in solving the model parameters. In the recursive process, the present invention uses an adaptive exponentially weighted moving average noise covariance estimator to quickly estimate the noise, adjust the noise covariance matrix of the recursive total least squares method in real time, reduce the influence of noise, and improve the compensation accuracy under the influence of geomagnetic vector error. At the same time, in the recursive process of the recursive total least squares method, the covariance matrix is ​​adaptively regularized to improve the compensation parameter estimation accuracy under the influence of multicollinearity.

The invention relates to the technical field of robot control, solves the technical problem that in the prior art, load parameter identification cannot be carried out on a large component in an existing industrial environment, and particularly relates to a robot tail end load dynamic identification method under a safe excitation track. Comprising the following steps: establishing a comprehensive expression between a load parameter vector and a non-contact force; establishing a dynamic excitation track of each joint of the robot in the feasible working space; and each joint of the robot transforms the posture of the end effector according to the dynamic excitation trajectory, and various parameters of the end load parameter vector of the robot are obtained through a total least square method based on the comprehensive expression. According to the method, the inertia tensor is considered in detail, so that the identification result error of the large-scale component is reduced, the error after external force compensation is further reduced, the external contact force is sensed more accurately, and the application possibility of the large-scale component in a contact type scene taking man-machine cooperation as an example is increased.

The invention belongs to the technical field of near-field RIS target positioning, and particularly relates to a non-line-of-sight near-field source parameter estimation and correction method based on decoupling atom norms. According to the method, through cross-correlation operation and pseudo-snapshot processing, angle information and distance information are decoupled while the array aperture is expanded. In addition, the single-snapshot decoupling atom norm algorithm based on the virtual aperture is provided while the advantage of super-resolution performance of the atom norm under the single snapshot is reserved. And through a decoupling atom norm method, the pitch angle and the azimuth angle on the RIS are further decoupled, and the algorithm complexity is reduced. And finally, designing a total least square parameter compensation algorithm based on an accurate model so as to reduce system errors caused by Fresnel approximation.

The invention belongs to the technical field of image detection, and discloses a finite element cloud picture crack fitting method based on direction priori and dark quantile ROI, which comprises the following steps: calculating a preliminary direction angle of a crack according to a reference point coordinate as the direction priori; taking the direction prior as a central axis direction and one of the reference points as a benchmark, constructing a strip-type ROI, extracting brightness values of pixels in the strip-type ROI, and screening the pixels with the brightness values lower than or equal to a threshold value as a preliminary crack candidate point set; calculating the local gradient direction of each pixel point in the candidate point set, screening out the pixel points of which the deviation between the local gradient direction and the direction prior is smaller than a preset direction tolerance, and forming a direction consistency set; and carrying out total least square fitting on the coordinates of the pixel points in the direction consistency set to obtain a main direction straight line and fitting parameters of the crack, and superposing the main direction straight line on the original finite element cloud picture to generate a crack fitting result. According to the method, robust and high-precision extraction of the crack direction in the complex finite element cloud picture is realized.

The application provides a space positioning method for mapping, and relates to the technical field of laser radar positioning. The method comprises the following steps: projecting color grid light to a region to be mapped by color coding, forming a positioning grid, and collecting two images of the on-off grid light by using an image acquisition device, extracting the grid intersection points as control points and obtaining the pixel coordinates of the control points by color recognition; emitting measuring light to the control points from at least three measuring light projection points, and solving the three-dimensional coordinates of the control points by space distance intersection; combining the pixel coordinates and the three-dimensional coordinates of the control points, and inversely calculating the pose of the image acquisition device by using a heteroscedastic total least squares model; identifying the pixel offset of a target feature point and its adjacent control point, combining the pose to convert the actual distance offset of the target feature point, and calculating the three-dimensional coordinates of the target feature point. The application effectively fuses optical active projection and optical ranging, and realizes efficient three-dimensional space positioning.

The application discloses an AR model parameter estimation method and device based on forward and backward linear prediction, and the method comprises the following steps: obtaining an interference signal of an infrared spectrometer and converting the interference signal into a matrix form, so as to complete construction of an AR model; a forward and backward expansion matrix of the interference signal is constructed by using a forward and backward prediction total least square method, and a forward and backward linear prediction equation is formed based on the forward and backward expansion matrix; the forward and backward expansion matrix is converted into a homogeneous linear equation by considering the noise disturbance influence of an observation vector and a data matrix, and a total least square problem of the homogeneous linear equation is constructed; and a singular value decomposition method is used to solve the solution of the total least square problem, so as to obtain parameter coefficients of the AR model. The application has the advantages that the problem of spectrum line separation of a restored spectrum is solved, the influence of noise on parameter estimation is reduced, and the accuracy of the restored spectrum is improved.

The invention discloses a wind power mixed tower outer surface flatness deviation positioning correction method and system, and the method comprises the steps: obtaining three-dimensional point cloud data and thermal imaging data of a wind power mixed tower, and carrying out the spatial synchronization processing; constructing a high-robustness reference curved surface and a space-time composite deviation field: performing cylinder fitting through a robust weighted overall least square method to obtain a reference surface, and fusing geometric and thermal information to generate the space-time composite deviation field; extracting a deviation region by adopting a multi-feature clustering and graph theory segmentation technology; aiming at the extracted deviation area, carrying out deviation cause auxiliary diagnosis by fusing a deep network and Bayesian reasoning; and in combination with a cause diagnosis result, performing adaptive correction decision optimization based on a multi-objective evolution algorithm, and outputting an optimal correction scheme considering quality, efficiency, cost and safety. The external surface deviation of the wind power mixing tower can be accurately positioned and corrected, and the detection efficiency and the correction reliability are improved.

The non-integer linear array DOA estimation method based on accelerated iterative hard threshold value comprises the following steps: constructing a model comprising a non-integer linear array and received signals thereof; generating an estimated value of an ideal covariance matrix of the received signal model; constructing a difference co-array and a sparse optimization model of the non-integer linear array according to the received signal model; solving the sparse optimization model by using an iterative hard threshold algorithm combined with an Armijo type step length criterion to automatically adjust the step length and a support set, and obtaining an initial angle estimation; and generating an accurate angle estimation by performing a first-order Taylor expansion on the initial angle estimation. The application expands the array degrees of freedom by constructing a difference co-array, takes a single fast sample covariance vector as a sparse representation input, adaptively accelerates the convergence by combining the iterative hard threshold algorithm and the Armijo step length criterion, and compensates for the off-grid error caused by grid division by using a Taylor expansion total least square method, so that high-precision DOA estimation is realized under a small number of fast shots.

The application discloses a clock-asynchronous-oriented electric metering error estimation accuracy improving method and system, and the method comprises the following steps: screening target electric energy metering data with normal load from electric energy metering data; constructing a clock-asynchronous deviation suppression strategy based on metering interval expansion; constructing an expanded electric energy conservation equation according to the clock-asynchronous deviation suppression strategy based on metering interval expansion and the target electric energy metering data, constructing a numerical definition parameter of the expanded metering interval length according to the electric energy conservation equation, and collecting a plurality of numerical definition parameters to form a parameter distribution; determining the length of a minimum effective expanded metering interval according to the parameter distribution; constructing an energy conservation equation set according to the length of the minimum effective expanded metering interval, and obtaining user metering error by solving the energy conservation equation set by using a total least square method. Therefore, the application can reduce the solving deviation introduced by data asynchrony to user metering error estimation, and realizes accurate estimation of user metering error.

A self-calibration method for frame non-orthogonality error and accelerometer error in a dual-axis frame inertial system is proposed. Under static base conditions, the platform is locked at 10 frame angular positions, accelerometer measurements are collected, and the specific force measurement of the inertial system in the platform coordinate system at each locked position is calculated. The attitude matrix of the inertial system from the platform coordinate system to the local horizontal coordinate system at each locked position is also calculated, yielding the acceleration of the inertial system in the local horizontal coordinate system at each locked position, which is used as the error observation. The residuals of the error coefficients are calculated using a total least squares algorithm, and the estimated values ​​of the measured parameters are corrected. Iterative calculations are performed until the iterative convergence criterion is met, obtaining the self-calibration results for each error coefficient. Through the self-calibration and compensation of the error coefficients, the accuracy of attitude measurement of the dual-axis frame inertial system carrier or base affected by the non-orthogonality error of the axis system can be improved.

The invention relates to a recursive Lagrangian total least squares estimator for estimating parameters of a physical system (100) considered to be linear, such as a motor vehicle. An estimation device (110) comprises a measurement unit (114) configured to obtain successive input measurements Ak and successive output measurements yk of the linear system, and an estimation unit (116) configured to estimate one or more parameters xk recursively from the input and output measurements. An estimation recursion k+1 comprises the following operations: (1), (2), (3), (4), (5), optionally (5'), (6), (7), where Qa,k and Qy,k are weighting matrices; δXk, Ãk and Pk are inter-recursion variables; ζk+1 is an instrumental variable correlated with the variables Ãk; Qz,k, ΔAk, Kk are internal variables; α is a regularisation term and λ is a forgetting parameter.

The invention discloses a lithium ion battery charge state estimation method based on a parallel battery neural network, and the method comprises the steps: obtaining battery charge and discharge data of a parallel battery under different working conditions, carrying out the preprocessing, carrying out the parameter identification through employing a total least square method, carrying out the branch current estimation through employing a DNN deep neural network, and carrying out the calculation of the branch current. And after the estimated current is obtained, SOC prediction is carried out based on a Sage-Husa adaptive filter in combination with an SRCKF (root mean square cubature Kalman filter) algorithm, and the state of charge of the battery is obtained. According to the fusion algorithm, firstly, a first-order ECM is constructed to capture the dynamic response characteristics of each parallel unit, and model parameters are identified by adopting a TLS (Total Least Squares) method, so that parameter deviation caused by measurement noise and system errors is reduced, and the modeling precision is improved. And finally, on the basis of estimating branch current, identifying model parameters and measuring voltage signals, introducing a Sage-Husa adaptive filtering algorithm and combining with a square root cubature Kalman filtering algorithm to realize online estimation of the SOC of each parallel battery.

The application discloses a transformer partial discharge positioning method based on multi-sensor abnormal time difference elimination, S1: installing a sensor for receiving a partial discharge signal on the surface of a transformer, and extracting the time when the sensor receives the partial discharge signal; S2: establishing a spherical positioning equation set under a space rectangular coordinate system and linearizing; S3: solving the linearized equation set, and obtaining an initial solution set after screening; S4: using a DBSCAN clustering algorithm to perform clustering analysis on the initial solution set, and obtaining multiple clusters and outliers; S5: classifying the clusters according to residuals, counting the measurement time difference frequencies corresponding to the clusters and the outliers, and identifying good time differences, ordinary time differences and abnormal time differences; S6: eliminating the abnormal time differences, considering the measurement noise of the remaining time differences, and establishing a constrained total least squares model; S7: selecting an iterative initial solution, and using a Newton iteration method to calculate a final positioning solution. The application can eliminate abnormal time differences and reduce noise influence, and thus still has higher positioning precision in a higher noise environment.

The invention provides a state monitoring and fault diagnosis system and method for a primary flue gas electrostatic dust collector of a converter. The system comprises a multi-source data acquisition module, a signal preprocessing and feature extraction module, an intelligent diagnosis core module and a man-machine interaction and early warning module. The invention further relates to a state monitoring and fault diagnosis method for the converter primary flue gas electrostatic dust collector. According to the invention, an improved PRONY algorithm is adopted to extract damping factors, oscillation frequency and amplitude characteristics, and a total least square method is adopted to improve parameter estimation accuracy; an integrated deep learning model combining a convolutional neural network, a long-short-term memory network and an attention mechanism is adopted to perform fault recognition, and a probability graph model based on a Bayesian network is adopted to perform fault positioning and reason reasoning; and the man-machine interaction and early warning module generates multi-level early warning information according to the diagnosis result and visually displays the multi-level early warning information. The fault diagnosis accuracy of the system reaches 94.5%-97.2%, the early fault early warning time is advanced by 12-72 hours, and the intelligent level of the dust removal system is effectively improved.

The application discloses a vehicle roll angle estimation method and device based on point cloud under critical working conditions and belongs to the technical field of intelligent automobile environment perception. Laser radar point cloud data and vehicle kinematics signals are acquired; a region of interest is dynamically adjusted according to the kinematics signals to adaptively focus on a ground plane; ground plane robust estimation is carried out in the dynamic region of interest based on a weighted total least squares method and a Huber loss function to obtain a ground plane normal vector; and the vehicle roll angle is solved according to the geometric relationship between the ground plane normal vector and a reference normal vector. The application solves the point cloud distortion problem caused by the violent movement of the vehicle body under critical working conditions, realizes stable and reliable estimation of the roll angle without increasing hardware, and effectively improves the adaptability of the perception system under extreme working conditions.

The invention discloses a non-integer linear array DOA estimation method based on an accelerated iteration hard threshold. The non-integer linear array DOA estimation method comprises the following steps: constructing a non-integer linear array and a received signal model thereof; generating an estimated value of an ideal covariance matrix of the received signal model; according to the received signal model, constructing a differential common array and a sparse optimization model of a non-integer linear array; an iterative hard threshold algorithm is utilized, the step length and the support set are automatically adjusted in combination with an Armijo type step length criterion to solve the sparse optimization model, and initial angle estimation is obtained; and performing first-order Taylor expansion on the initial angle estimation to generate accurate angle estimation. According to the method, the degree of freedom of an array is expanded by constructing a differential common array, a single-snapshot sample covariance vector is used as sparse representation input, convergence is adaptively accelerated in combination with an iterative hard threshold algorithm and an Armijo step length criterion, and off-grid errors caused by grid division are compensated through a Taylor expansion total least square method, so that the accuracy of grid division is improved. And high-precision DOA estimation is realized under a small amount of snapshots.

The invention belongs to the technical field of power distribution network planning, and particularly relates to a power distribution network acceptance capability evaluation method and system based on data driving, and the method comprises the steps: collecting the historical time sequence measurement data of each node of a power distribution network, calculating the instantaneous voltage sensitivity and the change rate of the instantaneous voltage sensitivity through the ratio of the measurement voltage variation to the measurement power variation, and calculating the acceptance capability of the power distribution network; combining the deviation between the voltage amplitude and the voltage safety upper limit to construct a dual coupling weight; constructing an improved total least square objective function, weighting the objective function in the whole time period by using double coupling weights, and identifying a dynamic voltage sensitivity sequence under the condition of applying physical constraints; and calculating a fitting residual error and a standard deviation thereof based on the dynamic voltage sensitivity sequence obtained by identification, constructing a global confidence upper limit of the sensitivity, and calculating the acceptance capability of the power distribution network by combining a voltage safety upper limit and the current operating voltage. According to the method, the admitting ability robust evaluation under the condition that parameters are unknown is realized.

The application relates to the technical field of antennas and discloses a high-precision two-dimensional DOA estimation method based on optimal redundant linear arrays, 1) input: array receiving data y i (t), optimal redundant parallel array two-subarray element position coordinates P S1 and P S2 ; 2) parameter calculation ① calculating array autocovariance matrices of two optimal redundant linear arrays according to a formula; ② calculating array cross-covariance matrices between the optimal redundant linear arrays according to a formula. The high-precision two-dimensional DOA estimation method based on optimal redundant linear arrays proposes an extended covariance matrix reconstruction algorithm, the algorithm constructs an extended covariance matrix containing autocovariance matrices and cross-covariance matrices, further constructs an extended covariance matrix reconstruction model, simultaneously realizes high-precision estimation of initial autocovariance matrices and initial cross-covariance matrices, and based on the initial autocovariance matrices and the initial cross-covariance matrices, utilizes a covariance vector sparse representation and a total least squares method in sequence to realize high-precision two-dimensional DOA estimation.