12 results about "Linear kalman filter" patented technology

The present disclosure provides techniques for measuring and compensating for clock drift errors in time-aware networks and time-sensitive applications, where a time-aware system (TAS) measures clock drift, and compensates for the measured clock drift, and makes predictions of future clock drift values based on history and other physical measurements. Existing messages used for measuring link delay and / or used for time synchronization can be used for frequency measurement (and thus clock drift measurement), and this measured drift can be applied as a correction factor whenever synchronization is determined and / or used. The predicted clock drift rate can be based on various probability distributions including linear, Kalman filters, and / or others. Other embodiments may be described and / or claimed.

A method for measuring and calibrating installation errors of a star sensor and an inertial navigation system relates to the technical field of integrated navigation, and specifically comprises the following steps: establishing a star sensor body coordinate system, an inertial navigation system body coordinate system and a space reference coordinate system; the method comprises the following steps: constructing an attitude transfer model containing an installation error angle based on a coordinate conversion relation of a star sensor and an inertial navigation system in space; an observation equation is constructed on the basis of an attitude matrix output by a star sensor and navigation parameters output by an inertial navigation system, and online identification and calibration of installation errors are achieved in combination with a linear Kalman filtering algorithm. The method does not need to depend on external reference equipment, accurate solving of the installation error can be completed through measurement data of the star sensor and the inertial navigation system carried by a carrier, the method is suitable for an inertia / starlight integrated navigation system of an aircraft, and the three-axis installation error generated when the star sensor and the inertial navigation system are fixedly connected and installed can be accurately identified and calibrated.

The invention relates to the technical field of computers, discloses an adaptive resolution and code rate video transcoding control method, and aims to solve the problems of lagging, image quality reduction, overhigh energy consumption and the like caused by network response lag, parameter decoupling control and neglecting of equipment state in the prior art. The method comprises the following steps: periodically acquiring network state, video content and equipment physical state data; predicting a future bandwidth by using a linear Kalman filter; extracting video space domain and time domain complexity features in real time; mapping a resource constraint level according to the equipment temperature and the electric quantity; and in a pre-established code rate-resolution-quality multi-dimensional model, combining the multi-dimensional information to solve an optimal transcoding parameter combination, and driving a transcoding engine to execute. According to the method, dynamic balance of fluency, image quality and energy efficiency is realized through collaborative optimization of millisecond-level bandwidth prediction and content awareness and equipment adaptive protection.

The invention discloses a complex power grid frequency-locked loop method and system based on a steady-state Kalman filter, and the method comprises the steps: constructing a harmonic decoupling network, a steady-state linear Kalman filter based on a dual-second-order generalized integrator, and a frequency-locked loop module with a self-adaptive function, collecting the voltage of a three-phase power grid, and carrying out the calculation of the three-phase power grid. The three-phase power grid voltage is sequentially input into the harmonic decoupling network, the steady-state linear Kalman filter based on the bisecond-order generalized integrator and the frequency-locked loop module with the self-adaptive function, and the estimated voltage angular frequency is output, so that the accuracy and the stability of the frequency-locked loop are improved, and more effective power grid voltage synchronization is realized.

This invention proposes a linear trajectory association method for traffic radar, overcoming the limitation of high computational complexity in conventional trajectory association using nonlinear Kalman filtering. Its main steps include: S1, associating trajectory m with point cloud data n, where the association threshold length corresponds to the extreme value of the second derivative of the k-distance function of point cloud data n; S2, for any associated trajectory m, calculating the measurement value of its corresponding point cloud data n, and calculating the measurement matrix Z of the current trajectory m to characterize the current measurement value; S3, constructing the prediction matrix A, measurement covariance matrix R, and prediction covariance matrix Q for the corresponding traffic vehicles; S4, calculating the linear Kalman filter, the estimation matrix X of the previous trajectory m, and the next estimation matrix X_, where X is estimated from the measurement value of trajectory m and the previous prediction value, and X_ is estimated from the current prediction value; S5, repeating the above steps until all trajectory associations are completed.

The application provides a power quality interharmonic detection method and device based on an electric energy meter, and relates to the technical field of power quality monitoring. The method comprises the following steps: acquiring continuous sampling data of a power grid signal, and preprocessing to obtain a complex frequency spectrum; adopting improved IEC grouping and adaptive threshold filtering to filter out false spectral lines, and generating a spectral component list; constructing a state space model and initializing a linear Kalman filter; using the linear Kalman filter to output optimal estimates of instantaneous amplitude, instantaneous phase and instantaneous frequency through prediction and update, and reconstructing the estimated value of the power grid signal; calculating the instantaneous estimation error according to the current sampling point data and the estimated value of the power grid signal; judging whether the power grid spectrum has a mutation according to the instantaneous estimation error and the maximum allowable error, and returning to step S1 to detect again based on the latest continuous sampling data when the mutation occurs. The scheme can improve the accuracy and adaptability of interharmonic detection.

The invention provides an electric energy quality inter-harmonic detection method and equipment based on an electric energy meter, and relates to the technical field of electric energy quality monitoring, and the method comprises the steps: obtaining continuous sampling data of a power grid signal, and carrying out the preprocessing to obtain a complex frequency spectrum; filtering false spectral lines by adopting improved IEC grouping and adaptive threshold filtering to generate a spectrum component list; constructing a state space model and initializing a linear Kalman filter; using a linear Kalman filter to predict and update the optimal estimation of the output instantaneous amplitude, instantaneous phase and instantaneous frequency, and reconstructing the estimated value of the power grid signal; calculating an instantaneous estimation error according to the current sampling point data and the estimated value of the power grid signal; s2, judging whether the frequency spectrum of the power grid suddenly changes or not according to the instantaneous estimation error and the maximum allowable error, returning to the step S1 when the frequency spectrum suddenly changes, and performing detection again based on the latest continuous sampling data. According to the scheme, the accuracy and the adaptivity of inter-harmonic detection can be improved.

The application relates to the technical field of digital management of road infrastructure, and discloses a road asset automatic positioning and digital filing method, which comprises the following steps: S1, data preparation, video data of a detection target is acquired by using a detection device to take pictures; S2, video frame picture processing, including target detection and tracking and depth estimation; S3, speed and position estimation, including speed and relative position estimation and absolute position estimation; S4, Kalman filter fusion, multi-source data is input into a linear Kalman filter for processing; and S5, digital filing. The application realizes the spatio-temporal data fusion of multiple sources and different structures by decoupling the target detection, depth estimation and motion analysis modules, and combining the Kalman filter with the inherent geometric prior information of the road environment. The automatic identification, distance measurement, absolute coordinate positioning and digital filing of road assets such as traffic signs can be completed through monocular camera acquisition, and high-precision automatic inspection and digital management of road assets are realized.

The application is suitable for the technical field of power internet of things, and relates to a power grid equipment measurement waveform distortion repair method and system, which comprises the following steps: dividing a power grid voltage signal sequence into multiple batches according to the cycle length; constructing a nonlinear observation model based on a sine function, and representing the power grid signal of each sampling point in the batch in the form of the nonlinear observation model; constructing an extended Kalman filter based on the nonlinear observation model; obtaining the state estimation value of each sampling point by using the extended Kalman filter; performing state transmission between adjacent batches, and adding pi after the phase parameter in the state estimation value at the end of filtering of the previous batch to serve as the phase parameter in the initial state estimation value of the next batch; and merging and outputting the repaired power grid signal. The application solves the problems of low filtering precision, poor waveform distortion repair effect and the like of the traditional linear Kalman filter, and has the advantages of high filtering precision, fast convergence speed, good batch processing continuity and the like.

The invention discloses an enhanced modeling and state estimation method for a turboshaft engine oriented to unknown noise, and aims to solve the problems of low modeling precision and large estimation error of a traditional model under health parameter degradation and unknown noise. According to the method, a state space model is established through a small perturbation method, then an enhanced linear model containing state and output compensation is constructed, and the prediction precision of an airborne model is improved; meanwhile, designing a noise acquisition module based on a Gaussian mixture model (GMM), converting unknown noise into Gaussian mixture distribution, and obtaining statistical characteristics; and finally, combining an enhanced linear model, a GMM (Gaussian Mixture Model) module and linear Kalman filtering to build a state estimation system. According to the method, high estimation precision can still be kept in a multi-health parameter degradation and unknown noise scene, the airborne real-time requirement is met, and the method is of great significance to engine fault diagnosis and health management.

A computer-implemented method for controlling a device, comprising the following: Receiving data x t from a sensor; Encoding the data x t via parameters of an encoder (102) to make a latent observation w t , for the data x t and an uncertainty vector σ w t for the latent observation w t to obtain; Processing the latent observation w t using a recurrent neural network to control a switching variable s t , which the weights α t determined to obtain a locally linear Kalman filter; Processing the latent observation w t and the uncertainty vector σ w t using the locally linear Kalman filter to obtain updated mean values ​​µ z t a latent representation z t of the Kalman filter and covariances Σ z t the latent representation z tto obtain the Kalman filter; Decoding the latent representation z t of the Kalman filter, to obtain an average value, µ x t , and a covariance Σ x t a reconstruction of data x t to obtain; Output of the data reconstruction x t at a time t; and Controlling the device based on the reconstruction of data x t .

This invention provides a method and system for human joint point heatmap triangulation based on temporal prediction, relating to the fields of computer vision and computer graphics technology. The method includes: Step S1: Starting a sub-camera process for each camera, synchronously running a 2D human pose detection network using a process-level parallelization framework, and outputting the pixel coordinates of human joint points; Step S2: Predicting the 3D positions of each joint point in the current frame using the 3D positions and velocities of the human joint points based on linear Kalman filtering; Step S3: Establishing a 3D joint point confidence heatmap based on the temporal prediction points, and calculating the final positions of the joint points in the current frame. This invention can be used for real-time, label-free, high-precision single-person human tracking, effectively improving human tracking accuracy in real-time scenarios.