1421 results about "Estimation result" patented technology

The invention discloses a classified processing pit-pond water storage amount remote sensing statistics joint estimation method, and relates to the technical field of water resource remote sensing monitoring, and the method comprises the steps: calculating the pit-pond density, and determining the grid width; calculating a normalized water body index, and extracting a pond water surface boundary; dividing the pit pond into an invertible pit pond and a non-invertible pit pond according to the water surface area and width of the pit pond; calculating the water storage amount of the invertible pit-pond by combining the area of the invertible pit-pond; calculating the water storage amount of the non-invertible pit pond by using the relationship between the water surface area of the non-invertible pit pond and the statistical model; and calculating the total water storage amount of the region, and generating a spatial distribution diagram of the water storage amount. According to the method, remote sensing inversion and a statistical model method are used for estimating the water storage amount, targeted processing can be carried out on different types of ponds, remote sensing images are combined with actually measured data, the water depth inversion precision of the invertible ponds can be effectively improved, and therefore a more reliable water storage amount estimation result is provided.

The invention relates to the technical field of bolt tightening, and provides an intelligent bolt tightening control method and system based on axial force measurement. Comprising the steps of collecting bolt identification and process parameter data, collecting temperature measurement data, and obtaining sound velocity compensation parameters and elastic modulus compensation parameters. And zero-load preloading data are collected and processed to generate zero-load baseline data, and a self-calibration model is obtained. Axial force data and ultrasonic echo data are collected, and an axial force fusion estimation result is obtained. And comparing an axial force fusion estimation result with target axial force data. And when in the target interval, fine twisting control is carried out based on model predictive control and a micro-stepping strategy, and the controllable damping unit is driven to carry out energy absorption and stopping. And acquiring an axial force fusion estimation result when the target axial force is reached, processing the data in the holding stage to obtain an axial force rebound check result, and if the axial force rebound check result exceeds the limit, performing secondary twisting control. According to the scheme, refinement and traceability of the bolt tightening process are achieved.

The invention provides a coherent signal arrival direction estimation method and device based on a deep convolutional network, and belongs to the field of array signal processing. The method comprises the following steps: receiving a to-be-detected signal containing a coherent signal by using a uniform linear array antenna to obtain an array receiving data matrix and extract a covariance matrix; forming an input feature vector by right upper triangular elements divided from a diagonal line in the covariance matrix, inputting the input feature vector into a covariance estimation model formed by a deep convolutional network, obtaining an estimation value of the right upper triangular elements under an ideal incoherent condition, and reconstructing the estimation value to obtain a covariance matrix estimation value; and performing characteristic decomposition on the covariance matrix estimation value, and generating a spatial spectrum by using a MUSIC algorithm to obtain an estimation result of the signal arrival direction. According to the method, the noise-containing mixed signal covariance matrix is mapped into the ideal incoherent noise-free signal covariance matrix through a physical constraint supervised learning framework, so that the estimation precision and robustness of the MUSIC algorithm in a coherent scene are improved.

The invention discloses a smart power grid state estimation method and system based on a space-time diagram convolutional network, and a medium, and the method comprises the steps: building a topological graph model of a power system according to the topological structure and measurement data of a smart power grid; an STGCN model containing a plurality of space-time convolution blocks is constructed, each space-time convolution block captures time dependence of time sequence data through a time gating convolution layer, and space features of the smart power grid are extracted through a space graph convolution layer; and establishing a power grid state estimation model based on the STGCN model to perform power system state estimation. According to the method, the accuracy and the reliability of an estimation result are ensured by fusing a space and time convolution technology. State estimation challenges under various topological structures can be effectively handled, stable estimation performance can be maintained, and a powerful guarantee is provided for safe operation of a smart power grid. And the operation efficiency and the response speed of the smart power grid are greatly improved.

The invention discloses a real-time dynamic three-dimensional reconstruction method and system based on patch matching and lightweight monocular depth estimation fusion, and the method comprises the steps: carrying out the PatchMatch matching of an input multi-view image pair, obtaining sparse depth information, and fusing the sparse depth information with a monocular depth estimation result; and the fused depth map is used for constructing a three-dimensional Gaussian point cloud, and high-quality three-dimensional scene reconstruction and new view rendering are realized. In the whole process, an end-to-end differentiable training framework is adopted, and the reconstruction precision and rendering efficiency of the 3D Gaussian point cloud in a dynamic scene are improved by optimizing a pixel-level Gaussian parameter regression network and a lightweight depth estimation model. The method can complete training and depth inference in a short time, is suitable for real-time or near-real-time application scenes, can be widely applied to the fields of virtual reality, augmented reality, film and television production and the like, and is particularly suitable for efficient three-dimensional reconstruction and new view angle synthesis of a target in a multi-view-angle dynamic scene.

The invention discloses a DOA (Direction of Arrival) estimation method and system based on a deep complex value convolution attention residual network, belongs to the technical field of array signal processing, and solves the technical problems of low precision and poor robustness of the existing DOA estimation method under the severe conditions of low signal-to-noise ratio, limited snapshot number and the like. The method comprises the following steps: acquiring data by using a co-prime array and preprocessing to obtain SCM data as original input information of DOA estimation; constructing a deep complex value convolution attention residual network to directly process covariance matrix information of a complex field, and utilizing an initial two-dimensional complex value convolution layer, a cascaded complex value convolution block attention network and a cross-layer residual connection structure to deeply extract complex value features related to a space angle; the DOA estimation module is responsible for finally mapping the extracted high-dimensional complex value feature vector to a representation space directly related to a DOA estimation task, and the output module converts the internal feature representation output by the DOA estimation module into a DOA estimation result which can be explained by a user.

The invention discloses an interactive multi-model underwater maneuvering target tracking method and system based on a pure azimuth second-order EKF. The method comprises the following steps: establishing a discrete state space model of a pure azimuth target tracking system; performing second-order linearization on the pure azimuth measurement model; calculating mixed input of a sub-filter corresponding to each model in the model set of the interactive multi-model algorithm; estimating a target state through parallel filtering of the azimuth-pure second-order EKF sub-filter corresponding to each model in the model set; updating the probability of each model in the model set; and combining the estimation results of all the sub-filters to obtain a final estimation result of the target state. According to the method, the target tracking precision can be improved, the problem of highly nonlinear measurement of the UUV on a target detection and tracking system through a passive sonar is effectively solved, good real-time performance is achieved, and real-time tracking of the UUV on the motion state of the underwater maneuvering target is achieved through adaptive matching of the real motion of the target through the interactive multi-model algorithm.

The invention discloses a GNSS (Global Navigation Satellite System) / inertial navigation tightly integrated navigation method and system based on robust auto-velocity constraint factor graph optimization, and the method specifically comprises the following steps: extracting pseudo-range and Doppler observation data from original observation information of a GNSS satellite, and obtaining accelerometer and gyroscope data from an INS (Inertial Navigation System); constructing a pseudo-range residual block, a Doppler residual block, an inertial navigation pre-integration residual block and a speed constraint factor, and integrating into a sliding window estimator based on factor graph optimization; according to the size of a set sliding window, adding all residual blocks in the window to form an optimized objective function; performing a first round of factor graph optimization to obtain a preliminary state estimation result, and endowing the observed quantity containing gross error with a relatively low weight; and updating the prior weight matrix, and carrying out second round of optimization to obtain a joint optimal estimation result of a plurality of moment states in the current time window. According to the method, the influence of gross error-containing observation information on weight estimation and state estimation is suppressed, the positioning precision is high, the robustness is strong, and the reliability is high.

The invention discloses a mining equipment positioning system and method based on radar, ultra wide band and inertial navigation, and the system is characterized in that a multi-source sensor unit, a data synchronization and collection module and a preprocessing module are connected in sequence, and a state fusion module is connected with the preprocessing module, a UWB ranging error compensation module, a radar motion estimation module and a pose output module; the upper computer is connected with the pose output module; the method comprises the steps of collecting multi-source data; timestamp alignment and format conversion processing are carried out, and feature and speed constraint information extraction is carried out on the point cloud data; fitting correction is carried out on the ranging data by adopting a weighted least square method, and position information is obtained through an error weighted average algorithm; feature matching is carried out, and in combination with a KISS-ICP point cloud registration method, a speed estimation result is obtained; carrying out data fusion, and obtaining a pose estimation result by adopting an extended Kalman filtering algorithm; and outputting equipment position and attitude information. According to the invention, high-precision real-time estimation of the attitude and position information of the mining equipment can be realized.

The invention provides a floating-point number calculation circuit, a floating-point number calculation method, a storage medium and electronic equipment, and relates to the technical field of computers. In the circuit, a mantissa calculation module is used for executing multiplication of two floating-point number mantissas to obtain a product mantissa when a floating-point number operation starting signal is received; the leading zero detection module is used for responding to the floating-point number operation starting signal and performing leading zero detection on the mantissa of each floating-point number to obtain a leading zero estimation result of the product mantissa; the normalization processing module is used for executing product mantissa displacement control and index adjustment based on the leading zero estimation result to obtain a mantissa operation result and an index operation result; and the result output module is used for outputting a floating-point number product result based on the mantissa operation result and the index operation result. The length of the floating-point number multiplication calculation path can be effectively shortened, and the floating-point number calculation efficiency is improved.

The invention relates to an intelligent measurement and control optimization method and device for dynamic parameter adaptive calibration, equipment and a medium. The method comprises the following steps: executing invariance causal test based on an external environment context to obtain a cross-environment invariant explanatory variable subset, and performing anti-fact simulation on each parameter in the cross-environment invariant explanatory variable subset to generate a causal attribution report; based on an under-excitation parameter subset and a cross-environment invariant explanatory variable subset in the baseline recognizable atlas, performing safe active excitation planning on the parameters to obtain a safe micro-perturbation excitation plan; and based on the residual error, the uncertainty estimation, the delay cross-correlation feature, the hysteresis loop area feature, the causal contribution score and the excitation-response fragment, performing calibration estimation on the target parameter by using a hierarchical estimator, and generating a calibration packet according to a calibration estimation result. By adopting the method, self-adaptive calibration of intelligent measurement and control dynamic parameters can be realized through residual attribution and cross-environment invariance test in combination with safe perturbation excitation.

Embodiments of the present application relate to the field of computer vision. Disclosed are a target six-degree-of-freedom pose estimation method and apparatus based on binocular vision and particle swarm optimization. The method comprises: obtaining a binocular image of a target, wherein the binocular image at least comprises a rotation matrix, a displacement vector and geometric features of the target; decomposing the rotation matrix into a rotation vector and a rotation angle by means of a rotation matrix decomposition method, and performing six-degree-of-freedom uniform sampling on the rotation vector and the rotation angle to obtain a data point set; establishing a mathematical relationship between the rotation matrix and the displacement vector on the basis of the geometric property of the geometric features and the data point set; and performing target pose estimation on the binocular image of the target by designing an objective function and an application optimization algorithm to obtain a pose estimation result. The present application solves the problems in the related art of low accuracy, poor universality and poor robustness.

The invention relates to the technical field of VR remote control, and discloses a VR remote control method and system based on gyroscope data fusion, and the method comprises the steps: obtaining current environment data, gyroscope three-axis data and accelerometer data; inputting the current environment data into a pre-trained error estimation model, and outputting an error estimation value and a prediction confidence coefficient; performing confidence coefficient judgment according to the error estimation value and the prediction confidence coefficient to obtain a zero offset estimation result; performing attitude analysis according to the gyroscope three-axis data and the accelerometer data to obtain a preliminary attitude estimation result; performing dynamic compensation according to the zero offset estimation result and the gyroscope three-axis data to obtain corrected gyroscope data; and performing fusion correction according to the initial attitude estimation result and the corrected gyroscope data, and outputting a final attitude estimation result. The method has the advantages that the accuracy of attitude estimation in VR remote control can be improved.

The invention provides an equipment running state optimization control method and device, electronic equipment and a storage medium. The method comprises the steps that historical data and real-time data of equipment operation are acquired, and key features related to the equipment operation state are screened out; preprocessing the historical data, dividing the preprocessed historical data into a plurality of categories, and generating a reference sample set of each category; performing weight calculation on the key features by using an analytic hierarchy process, and constructing a feature weight matrix; performing state estimation by using an improved multivariate state estimation algorithm; predicting the running state of the equipment according to the state estimation result, and judging whether the running state of the equipment is normal or not; and if the operation state of the equipment is normal, writing back an optimal control parameter generated based on the predicted operation state to the distributed control system. The parameter adjustment precision can be improved, the prediction precision can be improved, and the equipment operation efficiency can be improved.

The invention discloses a power distribution network topology state estimation method, electronic equipment, a medium and a product. The method comprises the steps of obtaining a topological structure and measurement data of a power system; constructing variable nodes and factor nodes according to the topological structure and the measurement data, and constructing a state-topological joint factor graph model according to the variable nodes, the factor nodes and the topological structure; based on the state-topology joint factor graph model, performing state estimation through a belief propagation algorithm to obtain an estimated value of state variable correction; if the estimated value of the state variable correction meets the convergence condition, correcting the topological state of the switch branch according to the active power and reactive power of the head end of the switch branch in the estimated value of the state variable correction; and outputting an estimation result of the topological state of the switch branch until the on-off state of the switch branch obtained according to the state variable is consistent with the original topological state of the switch branch. According to the method, asynchronous real-time updating of the topological state of the power system can be realized.

The invention discloses a sparse Bayesian parameter estimation method based on a Markov chain Monte Carlo (MCMC). The sparse Bayesian parameter estimation method is suitable for super-resolution target number detection and high-precision target parameter estimation in a communication perception integration (ISAC) scene. The method comprises the following steps: a base station transmits an orthogonal frequency division multiplexing (OFDM) signal and receives a target reflection echo; performing frequency domain transformation on the echo signal and constructing a high-dimensional observation vector; target parameters are modeled as random variables including path complex gain, time delay, Doppler frequency shift and angle, and a learnable parameterized sensing matrix is constructed; the sparse Bayesian framework is combined with MCMC sampling, the parameters are iteratively updated through random small-batch gradient optimization and the Metropolis-Hastings criterion, and finally the estimation result of the target parameters is output. According to the method, the precision limitation of a traditional gridding method is broken through, super-resolution parameter estimation is achieved, meanwhile, the calculation complexity and the storage overhead are remarkably reduced, and the method is suitable for the high-precision sensing requirement in a 6G communication system.

The invention discloses an Internet of Things equipment real-time early warning method and system based on digital twinning, and relates to the technical field of equipment operation management, and the method comprises the steps: collecting multi-modal original data of Internet of Things equipment, constructing a graph structure, and generating a semantic graph vector; obtaining a digital twinborn model state corresponding to the target equipment, forming an extended state vector by the digital twinborn state, the physical equipment state and the semantic map vector, and inputting the extended state vector into an extended Kalman filter for state fusion to obtain a state estimation result; and performing Monte Carlo simulation according to the state estimation result, generating a plurality of abnormal state samples, calculating a residual mean value between the abnormal samples and the current estimation result, correcting a covariance parameter of the extended Kalman filter, and generating an early warning tag. Fine estimation and risk level early warning of the state of the Internet of Things equipment are realized, and the state fusion precision and the abnormal response timeliness are improved.

The invention discloses a single-view unknown object 6D pose estimation method based on segmentation and new view angle synthesis, and relates to the field of object pose estimation. The method comprises the following steps: designing a segmentation module and a new view angle synthesis module of a pose estimation network, and obtaining a segmentation result of a target object by using the segmentation module; generating a target object multi-view-angle embedding template by using a new view angle synthesis module; matching the embedding representation zquery'of the segmentation result with a multi-view embedding template of the target object to find out the embedding representation most similar to zquery ', and determining a preliminary rotation attitude estimation result R of the target object; and estimating the relative translation vector t of the target object, and combining the R to obtain the relative pose (R, t) of the target object. According to the method, robust 6D pose estimation of the unknown object in a complex visual scene can be realized only by taking a single reference image as input, and the pose estimation precision and robustness of the unknown object in the complex scene are improved while the real-time requirement is met.

The present invention is a parameter estimation method for compartment model based on physics-informed neural networks. Starting from a physical model, the method extracts information from an AIF and a small amount of measurement data to obtain kinetic parameters, thereby greatly improving the scanning efficiency of a measuring instrument, and reducing occurrence of inaccurate estimation results due to patient movement. In addition, the present invention has the robustness to AIF noise and measurement data noise, and can flexibly arrange the time of data acquisition, reduce an error of inaccurate estimation caused by long time 10 acquisition and the patient movement, and improve the efficiency of data acquisition of the instrument. Experimental results show that the present invention is more stable and has less errors. Meanwhile, the present invention does not require the setup of training datasets, and is superior to an end-to-end supervised reconstruction method U-net network with fewer samples.

The invention belongs to the field of road vehicle control systems, and relates to a vehicle trafficability risk judgment method and system fusing driver ability and confidence. The system comprises a driving behavior and environment data acquisition module, a scene matching module, a traffic capacity estimation module, a traffic capacity scoring module, a driving behavior feature analysis module, a traffic confidence scoring module, a trafficability risk calculation module and a database. The scene matching module is used for carrying out similarity calculation on the basis of a historical traffic sample set and the structural features of the current traffic scene and screening similar samples; the traffic capacity estimation module is used for carrying out weighted fusion on the screened traffic capacity and the calculated matching weight to obtain a traffic capacity estimation result; and the trafficability risk calculation module is used for correcting the original trafficability risk based on the structure complexity and the vehicle performance by using the traffic capacity score and the traffic confidence score. According to the system, personalized and dynamic adjustment of traffic risks is realized, and the accuracy of trafficability risk prediction is improved.

The invention discloses an offshore rocket erection state anomaly detection method based on sparse representation and adaptive filtering, and the method comprises the following steps: obtaining state data in a rocket erection process through multiple sensors, constructing a time sequence, setting a sliding window with change capability in each time period, and extracting sparse features for state prediction. A feedback adjustment signal is formed by calculating the difference between a predicted value and an observed value, and parameters in the state estimation process and the external disturbance modeling process are adjusted respectively. A double-path structure is adopted to independently estimate the state and disturbance, and a final estimation result is obtained through a fusion strategy. The system continuously updates parameters according to error conditions, continuous self-adaptive adjustment and anomaly recognition are achieved, and the recognition precision and the response capability of the anomaly trend are improved. The method is suitable for the recognition processing of the abnormal state in the erection process of the rocket in the marine environment, and has high dynamic adaptive capacity and time sequence anomaly detection capacity.

The invention provides a sonar-assisted underwater navigation positioning method and system, and the method comprises the steps: carrying out dead reckoning according to an attitude angle outputted by an IMU and a speed outputted by a DVL, and obtaining a dead reckoning odometer factor; after the first frame is set as the key frame, if the time difference between the current frame and the previous key frame is greater than a time difference threshold value or the pose change of the current frame relative to the previous key frame is greater than a pose change threshold value, the current frame is set as the key frame; performing filtering, feature extraction and matching on the sonar image; performing pose extraction according to the matched feature points to obtain a sonar odometer factor; key frames in a preset range are searched for loopback detection, and a loopback constraint factor is obtained through matching; and performing back-end optimization on the to-be-optimized pose by using a factor graph optimization method. According to the method, dead reckoning results of the IMU and the DVL are adopted as odometer factors, and calculation of back-end optimization can be greatly simplified.

The invention provides a 3D human body posture estimation method and system based on dual-channel fusion driving in the technical field of computer vision. The method comprises the following steps: S1, constructing a 3D human body posture estimation model; s2, a large number of historical human body videos are acquired to construct a data set; s3, dividing the data set into a training set, a verification set and a test set, training the 3D human body posture estimation model through the training set, verifying the trained 3D human body posture estimation model through the verification set, and testing the verified 3D human body posture estimation model through the test set; s4, deploying the 3D human body posture estimation model passing the test; and S5, acquiring a real-time human body video, preprocessing the real-time human body video, and inputting the preprocessed real-time human body video into the deployed 3D human body posture estimation model to obtain a human body posture estimation result. The method has the advantages that the precision and robustness of 3D human body posture estimation are greatly improved.

The invention discloses a lightweight human body posture estimation method based on PLES-YOLO, and belongs to the field of computer vision, and the method comprises the following steps: carrying out the feature extraction of an input image through employing an improved down-sampling module, and obtaining an initial feature map; based on the initial feature map, a multi-scale feature fusion module is adopted to carry out progressive feature enhancement, and multi-scale fusion features are obtained; based on the multi-scale fusion features, performing feature optimization by adopting a spatial pyramid pooling module combined with an attention mechanism to obtain weighted multi-scale features; and on the basis of the weighted multi-scale features, a lightweight shared convolution detection head is adopted to predict human body key point coordinates, and a final attitude estimation result is output.

The invention belongs to the crossing field of artificial intelligence and mechanical engineering, particularly relates to a micro-motor fault prediction and health management system, and aims to solve the problems that early faults of a micro-motor are difficult to recognize, degradation modeling is inaccurate and maintenance lags. The system collects multi-source data through high-density sensing, combines denoising reconstruction, composite feature extraction and time-varying weighted fusion to generate health indexes, identifies health stages by using a segmented hidden Markov model, iteratively updates residual life prediction based on a Wiener process, outputs an estimation result with a confidence interval, and links a hierarchical maintenance strategy. And continuous optimization of the model is realized through federal learning. The system improves the fault early warning accuracy and prediction reliability, and reduces the operation and maintenance cost.

The invention discloses a forest carbon sink accurate monitoring system and method based on multi-source data fusion, and relates to the technical field of carbon sink monitoring. In order to solve the problems of low precision and poor timeliness of the traditional monitoring means, the adopted scheme comprises a multi-source data acquisition module used for acquiring satellite remote sensing, unmanned aerial vehicle aerial photography, ground monitoring sensor and meteorological data; the data pre-processing and fusion module is used for pre-processing the collected data and extracting and combining related features through a two-layer fusion method to serve as input data of a carbon sink estimation model; the model construction and optimization module is used for constructing and optimizing a carbon sink estimation model; the result visualization and analysis module is used for displaying a carbon sink estimation result, researching influence factors of forest carbon sinks and predicting a future change trend; and the integration and management module is used for integrating hardware equipment to construct a monitoring platform, developing a monitoring system based on cloud computing and the Internet of Things, and establishing and perfecting a management system. The method is used for monitoring the forest carbon sink.

The invention discloses an intelligent data processing method and system for geological disaster early warning, and relates to geological disaster early warning, and the method comprises the steps: collecting multi-source heterogeneous data; extracting topographic features of the geological disaster area according to the optical satellite image data and the laser radar data; performing differential interference processing on the synthetic aperture radar data to obtain differential interference phase data of the geological disaster area; performing phase unwrapping by using an improved minimum cost flow algorithm to obtain a continuous absolute phase value # imgabs0 #; performing nonlinear estimation on the estimation result of the surface deformation quantity to obtain state parameter estimation of the geological disaster area; and performing early warning detection by using a support vector machine (SVM). In order to solve the problem of low geological disaster prediction precision caused by difficult phase unwrapping in the prior art, the unwrapping problem is converted into a space-time network flow graph, space-time continuity constraints are introduced, the incidence relation between data in different periods is considered, a multi-scale strategy is adopted for solving and the like, and the prediction precision is improved.

The present application discloses a lightweight depth estimation method and system, belonging to the technical field of binocular system depth estimation, including: obtaining a depth estimation model, inputting a binocular depth image into the depth estimation model to obtain a depth estimation result. When obtaining the depth estimation model, determining the binocular depth image as an input parameter, performing feature extraction on the input parameter based on two-dimensional depthwise separable convolution, and obtaining a cost volume based on the extracted feature parameters, performing stacked hourglass processing on the cost volume based on three-dimensional pointwise convolution to obtain multi-depth feature information, and determining the multi-depth feature information as the depth estimation result, that is, the output parameter. The present application integrates lightweight feature extraction methods such as two-dimensional depthwise separable convolution and three-dimensional pointwise convolution, which can reduce the number of layers of feature extraction, reduce the number of parameters of the depth estimation model, and solve the technical problems of difficult deployment, low memory utilization rate, and waste of hardware resources of the binocular depth estimation network in actual application scenarios.

The present invention relates to the technical field of dynamic measurement of drill tool attitude, and discloses a dynamic measurement method and system for attitude parameters of a rotary steering tool. The method includes: obtaining in real time the three-axis angular rate, the three-axis earth gravity field components, and the three-axis geomagnetic field components of the rotary steering tool; determining a prior estimate value of the attitude angle according to the three-axis angular rate; determining a posteriori observation values of the attitude angle according to the three-axis earth gravity field components and the three-axis geomagnetic field components; obtaining a posteriori optimal attitude angle estimation result by fusing the prior estimate value of the attitude angle and the posteriori observation values of the attitude angle through iterative extended Kalman filtering; and calculating the attitude parameters according to the posteriori optimal attitude angle estimation result. In this application, an iterative extended Kalman filter is used to adaptively fuse the dynamic observables measured by a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer, so as to eliminate the noise interference of external environments such as high rotational speed, strong vibration, and magnetic anomaly faced during the dynamic measurement process, thereby improving the control and measurement accuracy of the rotary steering tool.

The invention discloses a sparse regularization direction of arrival estimation method based on a risk minimization principle, and belongs to the technical field of array signal processing and underwater acoustic signal processing. The method comprises the steps of receiving array signals and establishing an observation model; constructing a sparse representation and over-complete dictionary; establishing and initializing a regularization optimization model; carrying out adaptive weight updating and risk-driven parameter selection; after regularization parameters are determined, a fast iterative shrinkage threshold algorithm FISTA is adopted to carry out optimization solution, and dictionary refinement is carried out on the detected direction after each iteration convergence so as to reduce off-grid errors; and after a small amount of outer layer iteration is repeated, outputting a final DOA estimation result and corresponding power. According to the method, self-adaptive selection of regularization parameters and noise levels can be realized, and the problem of precision degradation under complex conditions of low signal-to-noise ratio, limited snapshot number, signal source correlation, power imbalance and the like is effectively solved without manual parameter adjustment, so that the robustness and practicability of estimation are remarkably improved.