35 results about "Robust filter" patented technology

The invention discloses a robust intelligent cooperative calibration method for an INS / DVL integrated navigation system. The method comprises the following steps: 1, establishing a traditional INS / DVLsystem model including a state equation and a measurement equation; 2, correcting the marine unmanned motion platform by adopting an alternate correction algorithm based on acoustic ranging; and 3, filtering an output abnormal value by using a robust Kalman filter based on statistical similarity measurement. The alternate correction algorithm based on underwater acoustic ranging does not need tobe supported by a reference station or a mother ship, so that the working range of the marine unmanned motion platform is expanded, and the positioning precision of the marine unmanned motion platformis further improved; and the calculated amount of the robust filter is smaller than that of an existing robust filter, the method has more advantages in navigation application with high sampling frequency, the robustness of a non-Gaussian scene caused by an abnormal value is higher than that of the existing robust filter, and the positioning precision of the method is superior to that of a traditional INS / DVL integrated navigation system.

The invention discloses a mixed interactive multi-model filtering method. The method comprises the following steps: firstly, building a system model and an observation model of an application object,building a total model set according to a change condition of noise statistic characteristics of external information, and describing current noise characteristics by adopting the adjacent three models; storing a robust filter real-time noise variance matrix in a historical sequence by adopting a sliding window, and adaptively selecting a proper model according to the smoothed noise statistic characteristics; setting an initial probability of each model and a mixed initial state and a covariance matrix of each filter, and according to the system model and the observation model, performing state estimation and covariance matrix updating processes, and performing model updating by adopting a Bayesian hypothesis testing method; and performing an output interactive process according to a weight, and outputting a final filtering result. By taking an inertia / Doppler combined navigation system as an example, the interference of noise sudden change type errors and outliers existing in Dopplerspeed measurement information can be effectively relieved.

The invention discloses a high-speed train set robustness filtering fault detection method considering event driving and output saturation and belongs to the signal processing field. The method comprises the following steps of establishing a high-speed train set state space model considering the output saturation and designing a trigger strategy; designing the robust filter of event driving and designing a filter gain in an off-line mode: setting the initial value of the filter, calculating a middle variable, calculating one step prediction state estimation error covariance upper bound, calculating the filter gain, calculating a state estimation error covariance upper bound, and calculating a state estimation value in an online mode: setting a state estimation initial value, calculating one step state prediction value, calculating information, and calculating a state estimation value; and designing a fault detection strategy, calculating a residual error, calculating a fault detectionstatistics amount, calculating a residual error secondary moment upper bound, calculating a fault detection threshold and setting a fault detection logic. By using the fault detection method, a systemfault can be detected in an online mode, and the safety and reliability of a high-speed train set are increased.

The invention discloses a pipeline geographic position information measurement method based on thick-tail robust filtering and relates to the technology of pipeline geographic position information measurement. The method is characterized in that an inertia / odometer wheel combined positioning system is composed of an MSINS and an odometer wheel, the inertia / odometer wheel combined positioning system is driven by a system power device to operate in a pipeline to obtain sensor data related to the trend of a pipeline, and strapdown inertia calculation and dead reckoning are conducted respectively,a difference value between the strapdown inertial calculation position and the dead reckoning position is used as the measurement information, the position measurement outlier information caused by odometer wheel slipping, sliding faults and pipeline motion constraint condition failure is filtered out through a thick-tail robust filter, moreover, strapdown inertia calculation errors are estimated, pipeline geographic position information output is corrected, and thereby a system can provide the continuous and high-precision pipeline geographic position information.

The invention discloses a rescue vehicle overall gravity center plane position estimation method. Plane positions of the gravity center of the rescue vehicle itself and the gravity center of a rescued vehicle are measured respectively; the plane position of the overall gravity center is acquired through derivation of a resultant moment theorem; and on the basis, a robust filtering algorithm is used for calculating the accurate estimation value of the overall gravity center plane position. The method of the invention can be used for safety monitoring and dangerous condition early warning for the rescue vehicle, and has the advantages of high precision, low cost, good real-time performance and the like.

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 a target estimation method based on multi-source information fusion, and the method comprises the steps: respectively observing the same target at the same time through a plurality of aircrafts, and obtaining the sight angle information between each aircraft and the target, the position information of each aircraft relative to the target, and the speed information of each aircraft relative to the target in real time; and sharing the optimal estimation state of the target obtained by each aircraft, and calculating the state information of the target at the next moment through a linear distributed consistent robust filtering algorithm.

The invention discloses a vehicle active safety control method and device. By establishing a state equation and an observation equation of robust filtering, a state vector and an observation vector are obtained, and the state vector and the observation vector are recursively calculated by filtering to obtain the first discrete moment The third vehicle mass and the third road longitudinal gradient, based on the third vehicle mass and the third road longitudinal gradient at the discrete time, the vehicle active safety control operation is performed. Through the above scheme, based on the robust filtering algorithm, the joint estimation of vehicle mass and road longitudinal gradient enables real-time and accurate acquisition of high-precision vehicle mass and road longitudinal gradient in the case of interference signals. By obtaining high-precision vehicle mass Quality and longitudinal slope of the road, and perform vehicle active safety control operations, thereby improving the control effect of vehicle active safety systems. In addition, while ensuring the high precision of vehicle quality and road longitudinal slope, it meets the application requirements under complex working conditions.

The invention relates to a spacecraft autonomous navigation robust filtering algorithm based on sequence image state expression, and belongs to the technical field of autonomous navigation. The method comprises the following steps: 1, constructing a spatial non-cooperative target autonomous relative navigation system state equation; 2, constructing a monocular camera sequence image measurement equation; 3, constructing a state expression model based on a sequence image, separating a measurement value from a measurement error value by using a trigonometric function formula, and obtaining a measurement noise expectation matrix epsilon k according to measurement error distribution; 4, obtaining a one-step state prediction value at the moment k and a one-step prediction covariance matrix Pkk-1 at the moment k; 5, calculating to obtain an optimal gain Kk of state estimation; step 6, calculating to obtain a state estimation value xkk and a state estimation covariance matrix Pkk at the moment k; according to the method, the anti-interference performance on the inaccurate initial value of the system state can be improved, so that a state estimation result with higher precision is obtained.

The invention discloses a fault detection method for a positive linear network control system based on a robust filter. Firstly, according to the current situation of networked control system with time delay and uncertainty, a discrete model of positive linear networked control system is established, and the corresponding robust filter is designed accordingly; based on this discrete model and filter, an augmented system is established, Then, by scaling the upper and lower bounds of the uncertain parameters, the augmented system is derived as a positive system, which is stable and satisfies l 1 / l ‑ (robustness / fault sensitivity) is a sufficient condition for performance; finally, through an iterative algorithm, combined with the optimization objective, the filter parameters are solved, that is, while taking into account the robustness and fault sensitivity, the fault of the positive linear network control system can be realized. detection. Through the iterative algorithm, the invention greatly widens the application range, improves the efficiency of fault detection, and can be applied to various sensory / non-sensor fault systems with uncertain parameters.

The invention discloses a high-speed train set robustness filtering fault detection method considering event driving and output saturation and belongs to the signal processing field. The method comprises the following steps of establishing a high-speed train set state space model considering the output saturation and designing a trigger strategy; designing the robust filter of event driving and designing a filter gain in an off-line mode: setting the initial value of the filter, calculating a middle variable, calculating one step prediction state estimation error covariance upper bound, calculating the filter gain, calculating a state estimation error covariance upper bound, and calculating a state estimation value in an online mode: setting a state estimation initial value, calculating one step state prediction value, calculating information, and calculating a state estimation value; and designing a fault detection strategy, calculating a residual error, calculating a fault detectionstatistics amount, calculating a residual error secondary moment upper bound, calculating a fault detection threshold and setting a fault detection logic. By using the fault detection method, a systemfault can be detected in an online mode, and the safety and reliability of a high-speed train set are increased.

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 relates to a no-gyro satellite gesture determination method based on tensor product multi-cell robust heavy hydrogen (H2) filtering, and belongs to the technical field of aircrafts. As for the nonlinear characteristics of satellite gesture dynamic equations and kinematical equations, the no-gyro satellite gesture determination method based on the tensor product multi-cell robust H2 filtering provides the multi-cell robust H2 filtering based on tensor product conversion to transform a nonlinear filtering problem into a linear filtering problem. The no-gyro satellite gesture determination method based on the tensor product multi-cell robust H2 filtering includes the steps of first establishing a state equation and a star sensor measurement equation of a gesture determination system and transforming a nonlinear system into a linear variable parameter error system by utilizing Jacobian linearization, then establishing linear parameter varying (LPV) system multi-cell model description according to tensor product model transformation and acquiring state estimation correction amount of the gesture determination system through combining with the robust H2 filtering, and last correcting gesture single-step pre-measurement acquired through a extended Kalman filter (EKF) method by utilizing the estimation correction amount to obtain a gesture estimation value. Accordingly, real-time updated filtering gain in the EKF method is avoid, and filtering calculated amount is reduced greatly.

The invention relates to the field of positioning technology, and discloses a positioning method based on single-step robust filtering of electronic compass course angle information, including setting the initial position of the pedestrian; starting the dead reckoning system for the pedestrian according to the initial position of the pedestrian; Acquire sensor data, store the compass sensor data in the compass observation data storage file; perform gait recognition based on the accelerometer sensor data; perform single-step electronic compass data sequence robustness filtering; determine whether to continue positioning. The invention performs single-step filtering processing on the heading angle data sequence of the electronic compass, which weakens the influence of the observation gross error caused by the abnormal magnetic field, reduces the noise in the heading angle observation data, and improves the robustness of the heading angle filtering result. The more reliable and stable heading angle filter value is used for trajectory calculation, thereby greatly reducing the calculation error, weakening the jump phenomenon of PDR trajectory, and improving the accuracy and continuity of PDR positioning results.