61 results about "Robust filtering" patented technology

A method of filtering to remove coding artifacts introduced at block edges in a block-based video coder, the method having the steps of: checking the content activity on every line of samples belonging to a boundary to be filtered and where content activity is based on a set of adaptively selected thresholds determined using Variable-Shift Table Indexing (VSTI); determining whether the filtering process will modify the sample values on that particular line based on said content activity; and selecting a filtering mode between at least two filtering modes to apply on a block boundary basis, implying that there would be no switching between the two primary modes on a line by line basis along a given block boundary. The two filtering modes include a default mode based on a non-recursive filter, and a strong filtering mode which features two strong filtering sub-modes and a new selection criterion that is one-sided with respect to the block boundary to determine which of the two strong filtering sub-modes to use. The two strong filtering sub-modes include a new 3-tap filter sub-mode and a 5-tap filter sub-mode that permits a more efficient implementation of the filter.

The invention discloses a system state estimation method based on an improved nonlinear robust filtering algorithm and relates to the technical field of space navigation. The method is based on a Huber nonlinear regression algorithm, so that high filtering accuracy and stable filtering output can be obtained under the condition that a measuring output equation is nonlinear, and measuring noises are in non-Gaussian distribution.

The invention provides an MCAUPF based target tracking method. An MCAUPF is used to complete state estimation in the target tracking process. In the target tracking process, target tracking equation and measurement equations are reconstructed into a nonlinear recursion model, in the framework of unscented particle filtering, a significance probability density function needed by filtering particlesgenerated by maximum correntropy unscented Kalman filtering is used, a Kullback-Leibler distance resampling method is used to resample the generated particles, the state of the tracking target is estimated according to a UPF algorithm flow, and the target is tracked in real time. The MCAUPF method is applied to target tracking in which outliers occur in noise measurement, the precision is highercompared with that of present PF, improved PF and robust filtering, and the computing complexity is lower than that of a present improved particle filter algorithm.

Embodiments of the present invention employ robust filtering at each of a number of scales, or resolutions, within a signal, such as a still image or video sequence. In certain embodiments of the present invention, robust filtering comprises or includes a non-linear neighborhood operation at each scale in order to produce both a denoised, sharpened and contrast-enhanced signal and a correction signal at each scale. Correction signals and sharpened and contrast-enhanced signals are combined to produce a denoised, sharpened and contrast-enhanced output signal. Scale-appropriate enhancements at each scale or resolution, including denoising, sharpening and contrast enhancement, ameliorate artifacts introduced by re-enhancement, at a second scale or resolution, features previously enhanced at another scale or resolution and inhibit over enhancement.

The invention relates to a nonlinear filtering method based on polytope differential inclusion, which belongs to the technical field of system filtering and control. The method comprises the steps of describing a nonlinear filtering error system with a PLDI (Programming Language Design And Implementation) model, converting a nonlinear filtering algorithm design problem to a linear uncertain system robust filtering algorithm design problem, designing a dynamic equation for estimated error correction solving by a hybrid robust H2/H-to-infinity filtering method, then designing a discrete nonlinear filtering equation by combining an EKF (Extended Kalman Filter) one-step prediction equation, and applying the discrete nonlinear filtering equation to a nonlinear discrete system to obtain the state estimation of the nonlinear discrete system in real time. According to the method, the nonlinear filtering design is simplified, the filtering gain is not required to be updated in real time, and the jacobian matrix is not required to be computed in real time in the implementation process, so that the computation amount is reduced greatly, the real-time performance of the nonlinear filtering is improved effectively, and the method is applicable to the design of a nonlinear filter.

The invention discloses an aerospace vehicle combined navigation robust filtering method based on a launching inertia coordinate system, belonging to the technical field of air vehicle combined navigation. The aerospace vehicle combined navigation robust filtering method comprises the following steps of performing inertial navigation algorithm arrangement by taking the launching inertia coordinate system as a reference coordinate system, and constructing a position, speed and posture calculation model of an air vehicle under the launching inertia coordinate system; on the basis of the position, speed and posture calculation model, constructing an equation of navigation system error states including a basic navigation parameter error and an inertia instrument error, and obtaining a posture linear measurement equation through a conversion module according to original star sensor measurement information; and finally estimating and correcting all state values in the constructed state equation by the robust filtering method. According to the aerospace vehicle combined navigation robust filtering method disclosed by the invention, the influence, caused by a geophysical field, on the navigation system of the air vehicle can be effectively reduced; furthermore, under a condition that the navigation system model of the air vehicle is inaccurate, a filter can work stably; therefore, the precision of a combined navigation system is improved.

The invention discloses a two-step robust filtering method and system of a GNSS/INS integrated navigation system. The method comprises the following steps: constructing a fading factor matrix according to information, constructing a gain coefficient matrix according to residual errors, constructing a state equation and a measurement equation of the GNSS/INS integrated navigation system, and fusingGNSS/INS data according to a Kalman filtering tight coupling mode; detecting whether the dynamic model is abnormal or not, and if the dynamic model exceeds a set threshold range, entering time updating through fading factor matrix adjustment; detecting whether the observed quantity is normal or not, if the observed quantity exceeds a set threshold range, adjusting an observation vector through multiple channels of a gain coefficient matrix, and entering measurement updating; and updating a Kalman filtering process, and outputting a GNSS/INS integrated navigation result. According to the method, the redundant information of the integrated navigation system can be fully utilized, meanwhile, scalar factors are expanded to a diagonal matrix, the algorithm complexity is reduced, and the positioning precision, tracking performance and stability of the integrated navigation system are enhanced.

The invention discloses a mean orbit element-based relative navigation robust filtering method. The method comprises the following steps: 1, deriving a relative navigation state equation according to relative kinematics of formation flying satellites, measuring the intersatellite real-time relative positions and speeds through using CDGPS, and filtering measured data through an EKF algorithm to obtain a relative mean orbit element; 2, outputting formation configuration parameters after filtering is stable; and 3, carrying out prediction and recursion according to a relationship among the relative positions, the relative speeds and the configuration parameters in order to obtain intersatellite relative positions.

The invention provides a collaborative AUV (autonomous underwater vehicle) navigation method based on robust information filtering. The collaborative AUV navigation method comprises the following steps: establishing a state equation describing an AUV navigation system and a measurement equation; estimating the state of a master AUV, and storing current time information in a state vector when a packet is transmitted to a subordinate AUV; performing information marginalization on the state vector after completion of data packet transmission; estimating the state of the subordinate AUV based on robust information filtering, receiving and processing data, performing distance measurement updating, namely performing the robust filtering process to correct distance measurement noises first and then performing the measurement updating process, and performing information marginalization on the state vector; restoring the information filtering states of the master AUV and the subordinate AUV toobtain AUV navigation information. By the collaborative AUV navigation method, the problem of low AUV positioning accuracy caused by abnormal measurement noises in underwater acoustic communication issolved, divergence of the navigation information is avoided, and the goal of high-precision real-time positioning in collaborative navigation is achieved.

The invention discloses an AUV cooperative navigation method of maximum correntropy adaptive cubature particle filter (MCACPF). The MCACPF is used for completing the state estimation problem in the AUV cooperative navigation process. During AUV cooperative navigation, a state equation and a measurement equation of the AUV cooperative navigation are reconstructed into a nonlinear recursive model, and then maximum correntropy cubature Kalman filter (MCCKF) is adopted in the framework of cubature particle filter (CPF) to generate an importance probability density function required in particle filter (PF), generated particles are then re-sampled by using a Kullback-Leibler distance (KLD) resampling method, and finally the estimation of an AUV state is obtained according to the algorithm flow of the CPF so as to realize the positioning of an AUV and complete the cooperative navigation. The MCACPF method is applied to the AUV cooperative navigation with outliers in measurement noise, higheraccuracy than existing PF, improved PF and robust filtering is obtained, and the computational complexity is lower than an existing improved particle filtering algorithm.

The invention discloses a method for estimating an attitude angle of a rescuing wrecker based on robust filtering. The method comprises the following steps: firstly, constructing a kinematic model of the rescuing wrecker according to the working characteristics of the rescuing wrecker, and then utilizing a robust filtering algorithm to acquire accurate estimated values of a pitch angle and a lateral inclination angle of the rescuing wrecker, on the basis of the model. According to the method provided by the invention, the pitch angle and the lateral inclination angle of the rescuing wrecker under the working condition of higher gradient can be estimated under the condition that the statistical properties of the interference signal cannot be accurately known. The method has the advantages of high precision, low cost, excellent instantaneity, wide application scope, etc., and can be used for monitoring the posture of the rescuing wrecker and early warning for the dangerous posture.

In a machine condition monitoring technique, a sensor reading is filtered using a switching Kalman filter. Kalman filters are created to describe separate modes of the signal, including a steady mode and a non-steady mode. For each new observation of the signal, a new mode is estimated based on the previous mode and state, and a new state is then estimated based on the new mode and the previous mode and state. In the steady mode, evolution covariances of both the observed signal and the rate of change of that signal are low. In the non-steady mode, the evolution covariance of the observed signal is set to a higher value, permitting the observed signal to vary widely, while the evolution covariance of the rate of change of the signal is maintained at a low level.

The invention discloses a robust filtering method and system for a key part of a motor train unit braking system, and the method comprises the steps: building a soft measurement model of the key part of the motor train unit braking system under the condition that the resolution of a sensor is limited, wherein the soft measurement model has random model uncertainty; determining a filter model of the key part of the motor train unit braking system according to a state equation and the soft measurement model of the key part of the motor train unit braking system, a current control input signal of the key part of the motor train unit braking system and a current measurement output single of the sensor; carrying out the state estimation through the filter model, and determining a state estimation correlation error. Therefore, the method can be independent of a model uncertainty structure, carries out filtering of the key part of the motor train unit braking system, improves the filtering robustness of the key part of the motor train unit braking system, and effectively guarantees application demands of the online monitoring of the operation state of the key part of the motor train unit braking system.

The invention belongs to the field of satellite analysis, and especially relates to a method used for increasing satellite attitude determination precision. The method comprises following steps: a uncertainty system model of a satellite attitude determination system is constructed based on the relative installation error of a star sensor; the uncertainty system model is subjected to standardization so as to obtain a Robust filtering algorithm iteration structure form; the weighting matrix in the Robust filtering algorithm iteration structure form is determined based on estimated error least squares optimization; a novel Robust filtering algorithm is constructed based on the Robust filtering algorithm iteration structure form and the weighting matrix; the novel Robust filtering algorithm isadopted for data processing of satellite attitude measuring data. The method is capable of inhibiting influences of star sensor relative installation error on satellite attitude determination, and increasing satellite attitude determination precision.

The invention belongs to the technical field of discrete linear system filtering, discloses a filtering method based on a norm regularization discrete linear system, and the discrete linear system, and uses l1-norm regularization to explicitly model and estimate an outlier and obtain a sparse solution. Due to the fact that explicit modeling is carried out on the outlier, the negative effect of theoutlier propagating through noise correlation can be estimated and reduced, and a more accurate estimation result can be obtained. According to the invention, the method provided by the invention provides the flexibility of allocating different penalty weights to the state outlier and the measurement outlier respectively, which is difficult to realize in a common M estimation method. By selectingproper regularization parameters, the proposed filter can be simplified into a linear optimal Kalman filter, so a good balance can be provided between robustness and optimality, and the effectivenessand superiority of the proposed robust filtering method are proved by using examples of simulation and experimental data.

The invention relates to a baffle plate constructed wetland system capable of reinforcing nitrogen and phosphorous removal, and belongs to the technical field of water treatment. The system is characterized in that a pool body is divided into two filler areas through a middle water fender; sewage enters from a water distribution trench on the front end of the first filler area and then carries outbaffling through a vertical clapboard to enter the second filler area; processed water flows out of the tail end of the second filler area. The compound filler added into the system is porous and hasa large specific surface area, a favorable space is provided for the apposition growth of microorganisms, pollutant removal can be reinforced through a powerful filtering, adsorption and flocculent precipitation function as well as iron and manganese elements, and system blockage can be effectively delayed. Meanwhile, the system can flexibly regulate the areas of two filler areas through the adding of the vertical clapboard according to water quantity and water amounts, water flow can be of a wave shape, and hydraulic residence time is prolonged. The system has the characteristics of good denitrification and phosphorous removal effect and low operation cost.

The invention discloses a robust filtering method of near space aerocraft transfer alignment model uncertainty. The method comprises the following four steps: step 1, based on working principle and characteristics of a near space aerocraft transfer alignment system, establishing a math platform misalignment angle error equation, a velocity error equation, a position error equation and an observation equation of the system; step 2, establishing a state equation and the observation equation of model uncertainty according to the error equation of the system; step 3, providing a state variable initial value (x0) and a forecast error variance matrix initial value (sigma 0|0), providing a sparse grid quadrature point set (zeta j, epsilon j; j=1, 2,... Np), and providing robust filtering parameters gamma and epsilon; and step 4, estimating system state by robust filtering, performing error correction for a secondary inertial navigation system, and finishing the transfer alignment process. The method provided by the invention is applicable for transfer alignment in the case that primary and secondary inertial navigation systems have model uncertainty when the near space aerocraft is in dynamic state.

The invention discloses an unmanned underwater vehicle target tracking method based on HuberM-Cubature Kalman filter. The unmanned underwater vehicle target tracking method based on the HuberM-Cubature Kalman filter comprises the following steps: 1) establishing a motion equation of an unmanned underwater vehicle; 2) establishing an observation equation of the unmanned underwater vehicle; and 3) using an UUV target tracking and localization algorithm based on the Huber-Cubature Kalman filter to establish the UUV motion model and observation model. The invention introduces a HuberM maximum likelihood estimation cost function into the Huber-Cubature Kalman filter and applies to the UUV target tracking and localization algorithm, and establishes the UUV motion model and observation model. Through reconstruction of CKF measurement information, and then filtering the nonlinear measurement information through a CKF algorithm, the interference of noise on the information is avoided, and nonlinear robust filtering is realized without the use of a traditional linearization method.

A filtration device includes features for robust filtering capability. In particular, the filtration device includes a filter medium having mixed matrix membranes that include hollow fiber membranes embedded with adsorbents such that the filter medium provides both mechanical and adsorption capability.

The invention provides a robust filtering method aiming at parameter uncertainty and observation loss. The robust filtering method comprises the following steps: 1, describing a question through a mathematical model, and modeling of norm-bounded parameter uncertainty and observation loss; 2, giving a filter model of undetermined parameters according to the mathematical model established in the step 1; 3, performing augmentation on an original state equation, solving a state covariance matrix after the augmentation, and constructing a novel state vector; 4, searching for an estimation error covariance of the original state vector according to the state covariance matrix after the augmentation in the step 3; and 5, setting the filter parameters, so that a minimum value is obtained at the upper bound of the estimation error covariance obtained in the step 4. Therefore, minimization of the upper bound of the state estimation error covariance is realized under the condition that a filter has the norm-bounded parameter uncertainty and observation loss.

Embodiments of the present invention employ robust filtering at each of a number of scales, or resolutions, within a signal, such as a still image or video sequence. In certain embodiments of the present invention, robust filtering comprises or includes a non-linear neighborhood operation at each scale in order to produce both a denoised, sharpened and contrast-enhanced signal and a correction signal at each scale. Correction signals and sharpened and contrast-enhanced signals are combined to produce a denoised, sharpened and contrast-enhanced output signal. Scale-appropriate enhancements at each scale or resolution, including denoising, sharpening and contrast enhancement, ameliorate artifacts introduced by re-enhancement, at a second scale or resolution, features previously enhanced at another scale or resolution and inhibit over enhancement.

The invention discloses environment-friendly paper-making equipment. The equipment comprises a pulping platform, a pulping box and an electric cabinet are arranged on the top of the pulping platform separately, a box cover is arranged on the top of the pulping box, a handle is arranged on the top of the box cover, a hinge is arranged on the outer side of the box cover, a buckle is arranged on theouter surface of the hinge, a discharging pipe and a water filtering pipe are arranged on the outer surface of the pulping box separately, a filtering box is arranged on the outer side of the water filtering pipe, an intercepting net is arranged in the filtering box, a filtering paper layer is arranged on one side of the intercepting net, an activated carbon fiber layer is arranged on one side ofthe filtering paper layer, supporting legs and a fixing disk are arranged on the bottom of the pulping platform separately, fixing screws and a connecting disk are arranged on the bottom of the fixingdisk separately, a driving motor is arranged on the bottom of the connecting disk, a driving shaft is arranged at the other end of the driving motor, and stirring blades are arranged on the outer side of the driving shaft. Therefore, according to the paper-making equipment, by means of the powerful filtering box and stirring blades, the purposes are achieved that the environmental friendliness property of the paper-making equipment is improved, and uneven stirring is prevented.

The invention belongs to the technical field of integrated navigation under non-ideal conditions, and particularly relates to an integrated navigation robust filtering method based on statistical similarity measurement. According to the method, the situation that normal DVL beam measurement information and large-error beam measurement information appear at the same time in an SINS/DVL tight combination system is considered, and aiming at the problem that normal measurement information is prone to being lost due to the fact that a robust filter of an existing combined navigation system processes the measurement information coarsely at present, a new robust filtering method is provided based on multi-dimensional measurement equation decomposition and statistical similarity measurement. According to the method, a statistical similarity measurement (SSM) theory is introduced while a multi-dimensional measurement equation of an SINS/DVL tightly integrated navigation system is decomposed, so that the measurement noise variance of each beam is assisted to complete respective self-adaptive updating when a large measurement error occurs, and finally, the independence of measurement information processing of each beam is ensured. The method can be used in the field of underwater vehicle integrated navigation under non-ideal conditions.