248results about How to "Improve filtering accuracy" patented technology

The scene matching method for raising navigation precision includes the following steps: 1. for the topographic scene matching module in combined navigation system to read in topographic data based on the digital topographic picture; 2. to search mapping point in the topographic picture based on the current geographic location the inertial navigation system obtains; 3. to intercept the reference map data from the topographic picture around the mapping point; 4. matching the measured map and the reference map; and 5. correcting inertial navigation with the matched result so as to raise the precision of inertial navigation system greatly. One simulating combined navigation system is also disclosed, which includes an aviation trace planning module, a flight control module, an aviation trace generator, an inertial navigation module, a topographic scene matching module matching module and an image displaying module. It has high locating precision, high autonomy and excellent man-computer operation interface.

The invention discloses a real time filtrating method for large-scale garbage message based on the content, including the steps as following: 1, pre-filtrating by using the black list and the white list; 2, carrying out the online filtrating by using the filtrating module based on the frequency; 3, carrying out the fast filtrating for the message content by using the method of twice hashing; 4, carrying out the pretreating of the message text for suspicion message, and converting the same into the phase vector; 5, judging the suspicion message by using the method of combination of Naive Bayesian classifier and support vector classifier. The invention can greatly improve the filtrating speed of garbage message, and efficiently reduce the produced erroneous judgement rate in the conventional key word filtrating method; can efficiently solve the problem of group sending garbage messages with malicious intent in the short time; can efficiently avoid to mistake the common message as the garbage message so as to reduce the erroneous judgement, and efficiently improve the filtrating accuracy of whole system by analyzing the message content on the semantics.

The invention discloses a self-adaption Kalman filtering method for autonomous navigation positioning of pedestrians. The method comprises the following steps: connecting an MEMS-IMU system integrating an accelerometer, a gyroscope and a magnetometer to a human body, and carrying out IMU data acquisition during movement of pedestrians; and establishing a self-adaption filtering model containing eighteen-dimensional state variables and nine-dimensional observed quantity, and carrying out recursive filtering while meeting four conditions, wherein a time varying noise statistical estimator is used for estimating and correcting system noise and observing the statistical character of noise in real time. According to the invention, on the basis of using zero-speed correction as error compensation correcting algorithm, a self-adaption filtering method fusing human body moving character is designed, noise interference signals caused by shake of the human body can be processed in real time, and the precision of autonomous navigation positioning of the pedestrians is effectively increased. The method disclosed by the invention is strong in stability and good in real-time property, and no extra hardware cost is increased.

The invention discloses an adaptive filtering method based on observation noise covariance matrix estimation. The adaptive filtering method comprises the following steps of: constructing a mutual difference sequence and a radial-normal distance difference sequence by utilizing measurement complementary characteristics of different measurement systems in a combined navigation system so as to carryout dynamic estimation on a measurement noise characteristic of a single epoch, measuring an error level of noise mapping according to an estimation result, and constructing an adaptive factor by taking a preset filtering precision as an index to effectively regulate a filtering gain array so as to carry out adaptive Kalman filtering resolution. According to the adaptive filtering method based onthe observation noise covariance matrix estimation, disclosed by the invention, high dynamic estimation of measurement noise characteristics is not only realized, but also the filtering gain array iseffectively updated, and finally the location precision of the combined navigation system is improved.

The invention discloses a self-adaptive filter method for a strapdown inertial/Doppler combined navigation system, which aims to improve the response speed and filter accuracy of a filter under a dynamic condition and improve the positioning accuracy of the strapdown inertial/Doppler combined navigation system. According to the method, a limited window smoother related to innovation covariance is introduced, a gain matrix in a filter can be directly corrected based on a smooth value of the innovation covariance, and a one-step predicted mean square error can be corrected by introducing a regulatory factor, so that the aims of improving the dynamic response speed and the filter accuracy of the filter can be achieved. The self-adaptive filter method disclosed by the invention can be applied to the strapdown inertial/Doppler combined navigation system, and the navigation positioning accuracy of the combined system under a dynamic condition can be effectively improved.

The invention relates to a simultaneous localization and mapping method based on distributed edge unscented particle filter. First, a coordinate system is built and an environmental map is initialized; then subfilters are built for each landmark point with successful matching respectively; next, based on a robot motion model, a particle swarm is generated in each subfilter respectively, and the state vector and the variance of each particle are obtained; noise is introduced, particle state vectors after extension are calculated by utilization of unscented transformation, the particles after extension are updated and the particle swarms are optimized; then particle weights are calculated and normalization is carried out, and aggregated data of each subfilter are subjected to statistics and the data are sent to a master filter; next, global estimation and variance are calculated; then the effective sampling draw scale and sampling threshold of each subfiter are determined, the subfilters with severe particle degeneracy are subjected to resampling; then the state vectors and the variances of the robot are output, and stored in a map. Finally, landmark point states are updated by utilization of kalman filtering algorithm until the robot is no longer running.

The invention discloses a strong tracking Kalman filter method for target tracking, belongs to the field of target tracking, and relates to a maneuvering target method based on a strong tracking Kalman filter. The method comprises the steps of building a disperse nonlinear dynamic system model; carrying out system initialization; carrying out time updating, and introducing a time varying and fading factor [lambda k]; measuring and updating; and finally carrying out filtering updating. According to the method, the time varying and fading factor is introduced into a Kalman filter, therefore, the method has the advantages that the Kalman filter is simple to realize and high in filter precision; meanwhile, the strong tracking Kalman filter has a real-time tracking capacity on the sudden change of the system.

The invention discloses an error processing method of the output signal of an optical-fiber scopperil component and belongs to the error processing methods of the output signal of an optical-fiber scopperil component in an inertial navigation system. The concrete steps of the processing method are that: an output signal model of the optical-fiber scopperil component and an error model thereof are built; the marking and the compensation of the fixed error of the optical-fiber scopperil component are implemented; the self-adaptive filtering of the random error of the optical-fiber scopperil component is implemented. The processing method effectively reduces the zero-offset drift of the optical-fiber scopperil component which is caused by temperature changes, has high marking precision of the fixed error, high marking efficiency and good adaptability; furthermore, the processing method effectively reduces the random error of optical-fiber scopperil, has small amount of computation, and is suitable for real industrial applications.

The invention discloses a filtering method and equipment of short messages and relates to the technical field of electronic information, which can flexibly deal constantly changed garbage messages, improves the classification precision of a classification model and filtering accuracy on the garbage messages and has no need of additionally establishing a training corpus for the classification model, thereby reducing cost. The filtering method of the short messages, provided in the embodiment of the invention, comprises the following steps of: judging the classes of the received short messages by utilizing the current classification model; when the short messages are garbage messages, filtering the short messages; when the short messages are normal messages, carrying out text finger extraction on the short messages to obtain finger information corresponding to the short messages; and when confirming that the short messages need to be audited according to the finger information and an auditing result is that the short messages are the garbage messages, updating the current classification model by utilizing the short messages, thereby executing next filtering process by utilizing the updated classification model.

The invention discloses a GPS/SINS/CNS integrated navigation method based on a five-order cubature kalman filter (CKF).The method is characterized by comprising the steps of 1, establishing an integrated navigation system non-linear error equation and a linear measurement equation based on a SINS error equation; 2, establishing the five-order CKF by means of the five-order spherical surface radial cubature rule; 3, filtering and fusing information output by a SINS, a GPS and a CNS by means of the five-order CKF to obtain the optimal estimation of navigation parameters.

The invention discloses a low pass filter, an active power filtering device and a harmonic detection method based on the active power filtering device.. The low pass filter is formed by an IIR (infinite impulse response) filter and an MA (moving average) filter in a cascade manner. The active power filtering device comprises a main circuit, a mutual inductance regulating circuit, an AD (analog-to-digital) sampling circuit, a detection circuit and a control circuit, wherein the main circuit is used for injecting compensation current to a three-phase power grid, the detection circuit utilizes the low pass filter, and the control circuit is used for forming a driving signal of the main circuit. Through utilizing the active power filtering device, the harmonic detection method disclosed by the invention comprises the following steps of: sequentially detecting, regulating and carrying out AD sampling on load current of the three-phase power grid; extracting harmonic current; and forming a driving signal according to the harmonic current. The invention prevents a problem that the separation of direct current amount and alternating current amount of active current and reactive current is poor in the traditional harmonic detection technique, improves the filtering precision, has quick dynamic response and can quickly and exactly detect the content of the harmonic current in the power grid.

The invention discloses an aero-engine gas path component health diagnosis method based on particle filtering. The aero-engine gas path component health diagnosis method includes the steps that a nonlinear mathematical model of an engine is established; a particle filtering algorithm is designed based on significance weight value adjustment of a neural network; finally, a gas path component health diagnosis is achieved based on the nonlinear model of the engine by the adoption of the designed algorithm. The nonlinear mode is that on the basis of a physical equation reflecting the aerothermodynamics performance of the engine, a shared working equation set among the components is established, and by the adoption of a Newton Laphson interactive algorithm, the nonlinear equation set is solved to obtain working parameters of the cross section of the engine; the particle filtering algorithm based on the significance weight value adjustment of the neural network is that a BP neural network algorithm and a typical sampling algorithm are combined, on the basis of a standard particle filtering algorithm, two steps of weight value splitting and particle adjustment are added, and therefore the phenomena of particle degradation and sample depletion are effectively avoided. The health diagnosis of gradual performance degradation and sudden faults of the gas path components within the service life of the engine can be achieved.

A method for revising integrated navigation system of Plough double star/ strap down inertial navigation based on adaptive closed loop H filter includes utilizing inertial measuring component in SINS / BDNS integrated navigation system to measure angular speed signal and linear acceleration signal of carrier, obtaining speed and position of carrier by Plough receiver in utilizing Plough satellite information, carrying out strap down inertial operation by navigation computer according to obtained information to let adaptive closed loop H filter carry out revision on integrated navigation system.

The present invention provides a multi-model self-calibration unscented Kalman filter method, the steps are as follows: 1: establish the basic equation of the system; 2: perform filtering initialization on the system; 3: perform time update on the system; 4: perform iterative variable update; 5 : perform measurement update; six: perform iterative calculation; through steps one to six, the present invention makes full use of the calculation results of the two methods of unscented Kalman filter and self-calibration unscented Kalman filter, relying on the Bayesian principle The multi-model estimation theory can automatically distinguish the unknown input as zero segment and non-zero segment, so that the most suitable result can be accurately selected as its prior estimate; the most important point is that the present invention is developed for strong nonlinear systems It is more widely used in engineering practical applications and has very positive application value.

The invention discloses a fuzzy Gaussian sum particle filtering method and device as well as a target tracking method and device. The fuzzy particle filtering method comprises the following steps: a state posterior probability density function, an observation noise probability density function and a state noise probability density function of a last target moment are established with a Gaussian sum method, the prediction probability density function of a target state of the current moment is acquired with Gauss-Hermite quadrature rules and the Monte Carlo principle, the particle weight and integral point weight of the current target observation moment are acquired with the fuzzy clustering principle, the weight of each Gaussian term is calculated for calculation of the mean and covariance of each Gaussian distribution, resampling is performed on the Gaussian terms, G Gaussian terms with larger weights are acquired, then the state posterior probability density function of the current target observation moment is acquired with the Gaussian sum principle, and particle filtering is finished. With adoption of the scheme, the filtering accuracy and estimation performance of a target state can be improved effectively.

The invention discloses a method of filtering an airborne LiDAR (Light Detection and Ranging) point cloud. The method comprises the following steps of firstly, carrying out gross error elimination and regular grid transformation on LiDAR point cloud data so as to generate a depth image; secondly, computing a segmented elevation threshold through an Otsu algorithm in an image threshold segmentation technology, and carrying out iterative rough classification of ground points and non-ground points on the point cloud data, which are obtained before regular grid transformation and resampling, through the threshold; lastly, respectively carrying out progressive triangulation network filtering on the classified ground points and non-ground points through the two different thresholds, and outputting network construction point cloud data, namely, ground point data. According to the method, the point cloud data, which participate in a filtering process, are data, which are obtained before regular grid transformation and resampling, so that the problem of accuracy loss of the point cloud due to regular grid transformation can be effectively avoided; a categorical attribute guidance is provided for the progressive triangulation network filtering, a filtering strategy is correspondingly adjusted for different terrain conditions, so that a better filtering effect is obtained.

The invention discloses a combination navigation filtering method of a multi-model underwater vehicle. The navigation filtering method provided by the invention comprises the following steps of firstly establishing a state equation, an observation equation and a noise equation of a SINS/DVL/TAN/MCP combination navigation system according to a underwater vehicle combination navigation system; determining a model set according to a system equation and a noise model; selecting characteristic variable from the combination navigation system, and establishing a bayesian network; and correcting the model switching probability in multi-model estimation by adopting a bayesian network parameter according to a multi-model filtering algorithm structure, and calculating the estimation fusion of a filter in a weight sum manner. The data processing and resolving operations of the combination navigation are finished by a navigation computer according to a filtering model and an algorithm flow of the combination navigation system. The navigation filtering method provided by the invention has the advantages of being capable of improving the filtering accuracy of the combination navigation system under a complicated environment, and strengthening an autonomous navigation positioning property of the underwater vehicle.

The invention discloses an adaptive point-cloud filtering method for a complex terrain structure, and solves problems in the prior art that a filtering algorithm is sensitive to parameter change and misclassification is severe because of the fluctuant change of the terrain and the complex surface structure of an urban region. The method comprises the following steps: (1) building a point-cloud pyramid; (2) initializing a ground point G through employing the roughest hierarchical pyramid data, adding data of the next stage to a to-be-classified point U, and updating to-be-classified points; (3) carrying out the iteration of all hierarchies of the point-cloud pyramid, processing all the to-be-classified points, and storing a newly added ground point; (32) employing the method to obtain a filtering threshold value and carrying out filtering according to a DEM, bent energy and the scale information of the pyramid, and distinguishing the ground point and a non-ground point; (33) repeatedly carrying out steps (31)-(32) till there is no newly added ground point; (4) storing the ground point and the non-ground point after classification.

The invention discloses a ballistic trajectory forming method based on output correlation adaptive Kalman filter. During the ballistic trajectory forming process, an output correlation functional matrix is estimated by the utilization of bullet flying parameters, and the optimal steady-state solution of Kalman filter gain matrix is calculated by the correlation functional matrix so as to make the steady-state gain fit in with an actual observed value; and the correlation functional matrix is continuously adjusted according to the observed value by a recursion method, and adaptive filtering estimation and correction of trajectory data are realized so as to obtain accurate ballistic trajectory. According to the invention, the filter divergence problem caused by factors such as incomplete consistency between a trajectory attribute model and an actual physical model, inaccurate system noise and observation noise parameter estimation and the like is inhibited effectively, and filtering accuracy and accuracy of the ballistic trajectory are raised.

The invention discloses a linear FLL provided method for controlling a photovoltaic inversion adjuster, and belongs to the technical field of electric system control. The linear FLL provided method for controlling the photovoltaic inversion adjuster aims at applying the FLL synchronization technology to solving the problems that in the prior art, a photovoltaic grid-connected inverter is low in adjuster control accuracy and complex in implementation under the grid voltage distortion condition. The linear FLL provided method includes the steps that the grid three-phase voltage is firstly converted, then telescoping summing is carried out on classic SOGIs to obtain a simplified type first-order generalized integrator, obtained difference value signals are led out, the average value of the difference value signals is calculated and then input into an FLL, clarke inversion conversion is carried out on obtained positive-and-negative sequence voltage components to obtain three-phase voltage positive-sequence components under an abc coordinate system, and finally an output frequency estimated value is integrated to obtain the positive sequence phase. According to the filtering adjuster, the positive-and-negative-sequence separating calculating link is not needed, grid synchronous detection is not influenced by grids, and the linear FLL provided method is particularly suitable for the field of photovoltaic inverter control under the grid distortion condition.

The invention discloses an automotive radar target tracking method of iterative square root CKF (Cubature Kalman Filtering) on the basis of noise compensation. The method comprises the following stepsthat: firstly, setting a system initial value, and calculating a cubature point value in a time update stage; spreading the cubature point; estimating a one-step prediction state and an error covariance square root factor; in a measurement update stage, importing a Gauss-Newton nonlinear iteration method to carry out iteration update, and calculating the cubature point during each-time iteration;spreading the cubature point; calculating measurement estimation; calcauting the square root factor of an innovation covariance and a cross covariance matrix; calculating Kalman gain; updating a current iteration state, and estimating the square root factor of an error covariance; judging whether an iteration termination condition is achieved or not; updating a current state, and estimating the error covariance square root; and in the measurement update process, regulating the noise compensation factor to optimize state estimation. By use of the method, accuracy and stability in an automotiveradar target tracking process can be effectively improved.

The invention discloses an integrated navigation fault tolerance method based on adaptive federal strong tracking filtering, comprising the following steps: establishing an integrated navigation system state model; constructing a fault detection function for detection; constructing an adaptive strong tracking filtering algorithm for state estimation; sending output data of a fault-free subfilter to a main filter for adaptive information fusion to obtain a global state estimation value; feeding the global state estimation value back to a fault-free subsystem, and resetting, for a subsystem withfault, subsystem information; and correcting, based on the obtained global state estimation value, navigation parameters output by an inertial navigation system. The method can not only improve the filtering accuracy of the system, but also improve the tracking ability of the filter when the system state changes suddenly, can detect and isolate the faulty system, improve the accuracy and reliability of the fusion result, and thus enhance the robustness of the integrated navigation system. The method is simple and easy to implement.

The invention discloses an alignment method of an underwater large misalignment angle based on SINS (Strapdown Inertial Navigation System)/DVL (Doppler Velocity Log) of SRQKF (Square-root Quadrature Kalman Filter). The alignment method comprises the following steps: step 1: establishing a nonlinear error model and a nonlinear filtering equation of the SINS under the large misalignment angle; step 2: constructing the SRQKF by utilizing a multivariate Gauss point and coefficient configuration method and a square-root filtering method in Gauss-Hermite quadrature; and step 3: estimating the misalignment angle by utilizing the SRQKF, and correcting a strapdown attitude matrix, thus obtaining accurate strapdown attitude matrix and attitude angle. The alignment method disclosed by the invention has the advantage that the underwater alignment accuracy and alignment speed of the carrier strapdown system are improved.

The invention discloses an improved mixed Gaussian particle filtering method used in an inertial integrated navigation system. The method comprises the following steps of: according to the characteristics of inertial integrated navigation, establishing a state equation, an observation equation and a noise model of a strapdown inertia navigation system/global position system (SINS/GPS) integrated navigation system; and according to the characteristics of the model, establishing a mixed Gaussian particle filtering algorithm structure. According to the algorithm structure, the particle filteringis divided into two steps; and Gaussian distribution parameters are acquired in the updating process of the mixed Gaussian particle filtering state by adopting an unscented kalman filtering (UKF) algorithm. The state updating and other part of algorithms are implemented by adopting the particle filtering algorithm; and the flow path for implementing the mixed Gaussian particle filtering is provided. A navigational computer performs data processing and resolving operation of the integrated navigation according to a filtering model and the algorithm flow path of the inertial integrated navigation. The method has the advantages of improving the filtering precision of the integrated navigation system, reducing the filtering time to a certain extent and solving the non-linear filtering problemof the integrated navigation system better.