380results about How to "Avoid divergence" patented technology

The invention discloses a multi-system dynamic PPP resolving method based on robust self-adaption Kalman smoothing. The method includes the steps that receiving machine outline coordinates and receiving machine clock bias of all systems are solved through selecting-weight-iteration pseudo-range single-point positioning, and accordingly all positioning error correction values are calculated according to an error correction model in combination with the satellite precise ephemeris and satellite precise clock bias; strict data quality control is conducted on observation data. Due to the fact that dynamic PPP accuracy is easily affected by undetected small cycle slips or the gross error and the like, an observation equation weight matrix is adjusted according to the observation value residual vectors, and the undetected small cycle slips or the gross error and other influence factors are removed; self-adaption factors are determined according to the state predictive information, and thus the influence on parameter estimation of the predictive information is controlled. By means of the method, when multi-system dynamic PPP is conducted through a single receiving machine, the feature that the number of multi-system satellites is increased greatly, on the basis that the stability of the satellite structure is guaranteed, the influence of the gross error is weaken effectively, the dynamic noise abnormity in dynamic positioning is improved, and finally the high-precision and high-stability multi-system dynamic PPP result is achieved.

The invention provides a GNSS/INS/vehicle integrated navigation method for agricultural machinery operation in the technical field of navigation. The method comprises the following steps: S1, inertial navigation calculation is carried out after correction of zero offset of an inertial measurement unit (IMU), and a state transition matrix and a system covariance matrix are calculated through Kalman filtering; S2, whether GNSS information is updated is judged; if the GNSS information is updated, the GNSS information is extrapolated, filtering estimation is carried out by taking the position and speed differences between INS and GNSS as observations, the dynamic condition of the carrier is judged, correction platform error and IMU zero offset error are fed back, and the method returns to S1 for loop execution; or, the current information is the position, speed and attitude information of the carrier; and S3, whether vehicle information is updated is judged; if the vehicle information is updated, the vehicle information is extrapolated, filtering estimation is carried out by taking the speed difference between INS and the vehicle as an observation, and correction platform error and IMU zero offset error are fed back; or, the current information is the position, speed and attitude information of the carrier. The method has high navigation accuracy, and can achieve high navigation accuracy with a low-cost IMU.

The invention discloses a real-time navigation system and a real-time navigation method for an underwater structure detection robot. The navigation system comprises a magnetic compass, a gyroscope, an accelerometer, a depth meter and a navigation microprocessor, wherein the magnetic compass, the gyroscope, the accelerometer and the depth meter are used for respectively collecting magnetic field intensity, an angular speed, a linear speed and submerged depth data and transmitting the magnetic field intensity, the angular speed, the linear speed and the submerged depth data to the navigation microprocessor; the navigation microprocessor is used for calculating attitude and position of the underwater robot according to the collected data. The navigation method comprises an attitude algorithm, a speed algorithm and a depth algorithm; according to the attitude algorithm, a complementary filtering method, a quaternion gradient descent method and a Kalman algorithm are combined for obtaining an attitude matrix and an attitude angle; the speed algorithm is used for calculating the speed and the position of the robot by using a three-order upwind scheme with rotary compensation; the depth algorithm is used for processing the data of the depth meter by using a moving average filter algorithm so as to obtain the submerged depth. By virtue of the real-time navigation system for the underwater structure detection robot and the method thereof, the navigation cost is reduced and a relatively good navigation precision is achieved.

A method for active noise cancellation in a volume generates a mathematical model of a noise process to be cancelled. Using the mathematical model, a noise signal in a next sample period is predicted from a measured noise signal. The predicted signal is inverted and applied to the volume. Destructive interference of the noise signal and the inverted signal cancels the noise in the volume.

The invention discloses a CKF filtering-based vehicle dynamic model auxiliary inertial navigation combined navigation method. The CKF filtering-based vehicle dynamic model auxiliary inertial navigation combined navigation method comprises the following steps: calculating posture, speed and position of a vehicle according to angle increment and specific force output by a micro-inertia device and by an inertial navigation numerical value updating algorithm; establishing a three-degree-of-freedom vehicle dynamic model, and calculating the speed of a carrier by taking a steering wheel angle and a longitudinal force as control input quantity and by a fourth order Ronge-Kutta method in real time; designing a CKF filter by taking an inertial navigation equation as a state equation and speed difference between a dynamical model and inertial navigation calculation to perform state estimation on a combined navigation system; performing output correction on strapdown inertial navigation calculation result by the position the speed and the posture error obtained by CKF estimation, and performing feedback correction on the inertial navigation through peg-top and adding error. The method aims at the problems that the inertial navigation error is accumulated along with time and navigation precision cannot be maintained for a long time, and the accuracy and the reliability of a vehicle navigation system can be improved.

The invention discloses an internal temperature estimation method for a lithium battery based on a discrete sliding-mode observer. The method comprises the following steps that a lithium battery discrete state space model for internal temperature estimation is established; a working voltage, a working current, a surface temperature and an external environment temperature of the lithium battery under a working condition are collected in real time; parameters of the lithium battery discrete state space model are identified online; and the discrete sliding-mode observer is constructed to estimate the internal temperature of the lithium battery in real time. The method has a good temperature estimation effect, can ensure the convergence of the observer strictly, and is highly robust for modeling errors, model parameter perturbation and external disturbance of the lithium battery thermal model.

The invention relates to a strong-tracking self-adaptive-square-root-quadrature-Kalman-filter (SQKF)-based state of charge (SOC) estimation method of an emergency lamp battery. The method comprises: establishing a second-order RC equivalent model of an emergency lamp battery; on the basis of the battery equivalent model, establishing a corresponding discrete state space model equation; and carrying out filtering estimation on the SOC value of the battery by the strong-tracking self-adaptive SQKF method. Compared with the existing SOC estimation method under the Kalman filtering framework, the provided method has high estimation precision. Meanwhile, with introduction of a time-varying fading factor and an on-line estimation system noise variance, a filter divergence problem caused by unknown time varying of the noise statistical characteristic during battery system modeling can be effectively suppressed.

The invention relates to a vector search iterative matching method based on inertia/gravity matching integrated navigation, the vector search iterative matching method is characterized by comprising the following steps of estimating a matched search scope and a current matching performance by an inertial navigation output location; taking a correlative extremal function as a target search to determine an optimal matching transformation; performing constant weighted iteration on the result of matching position to obtain an optimal matching position finally. The vector search iterative matching method provided by the invention serves as a hub for connecting component parts of a gravity matching system; a calculating process from gravity measurement information in real time to a carrier position can be completed; an inertial navigation error range is taken as a matching scope; a global optimum solution can be obtained, and divergence can be restrained; the requirement of an initial position error is low; the error and divergence problems caused by database linear processing of gravity data can be effectively avoided; the efficiency and the property of an algorithm are improved, and the vector search iterative matching method has a strong robustness on an gravity measurement random error and an inertial navigation system variable error.

A method to increase low-energy beam current according to this invention is applied to an irradiation system with ion beam comprising a beam generation source, a mass analysis device, a beam transformer, a deflector for scanning, a beam parallelizing device, an acceleration/deceleration device, and an energy filtering device. The beam transformer comprises a vertically focusing DC quadrupole electromagnet QD and a longitudinally focusing DC quadrupole electromagnet QF. The beam transformer transforms a beam having a circular cross-section or an elliptical or oval cross-section to the beam has an elliptical or oval cross-section that is long in the scan direction in all the region of a beam line after deflection for scanning.

The invention relates to the field of resisting magnetic interference, in particular to a method for resisting magnetic interference. The method includes that magnetic-field strength changing, magnetic inclination changing and magnetic-field component changing of a geographic system are used at the same to judge whether magnetic-field interference exists or not, so that whether the magnetic-field interference exists or not can be judged more accurately; when a magnetic field has the magnetic-field interference and the magnetic-field interference lasts for a long time, course angle divergence caused by solely using a gyroscope for integral computation of a course angle for a long time is avoided; under this circumstance, magnetometer data are recorded and utilized to correct the course angle, so that the course angle divergence is avoided; when the magnetic field has the magnetic-field interference and the magnetic-field interference lasts for a long time, due to the course angle correcting process, interference of the magnetic-field interference on course angle measuring accuracy can be effectively weakened and controlled, and the measuring accuracy of the course angle is guaranteed.

The invention discloses a carrier tracking loop based on a self-adaptive second-order Kalman filter and a filtering method of the carrier tracking loop. The carrier tracking loop based on the self-adaptive second-order Kalman filter comprises a local carrier generator, an integral clearer, a carrier loop phase discriminator, a loop filter and the self-adaptive second-order Kalman filter. The invention overcomes the defect of tracking loop lock losing caused by divergence of the traditional Kalman filter when priori information is inaccurate. When a system model is incomplete or the priori information is inaccurate, a forecast error covariance matrix is self-adaptively regulated through fading factors, thus the purpose of regulating a gain matrix in real time is achieved, the filter approaches the optimal, and the divergence of the filter can be effectively inhibited. Meanwhile, the carrier tracking method has the advantages of simpleness in calculation of a method of estimating the fading factors, small calculated amount, strong adaptability and reliability improvement of a filtering algorithm.

The invention discloses a robot positioning method and device based on SLAM (Simultaneous Localization And Mapping) of covariance intersection fusion. The method comprises the steps of collecting an image of a surrounding environment through a binocular camera of the robot and correcting the image to obtain the corrected image; processing the image, and further constructing a corresponding map point to obtain a state space model of the robot and an observation model of the robot on a road sign; predicting a predicted value of the robot posture at the current moment according to an optimized estimation value of the robot posture at the previous moment; obtaining an observed quantity at the current moment according to the observation model of the robot on the road sign; performing correctionupdate by use of a CI fusion-based filter method in combination with the observed quantity and the predicted value at the current moment to obtain the optimized estimation value of the robot postureat the current moment. According to the robot positioning method and device based on SLAM of covariance intersection fusion, the covariance intersection fusion algorithm is applied to the SLAM, thereby enhancing the robustness in the linear error, improving the filter precision and improving the accuracy of positioning of the robot in different practical application environments.

The invention belongs to a POS post-processing method, and specifically relates to a POS post-processing method with the combination of closed-loop forward filtering and reverse-smoothing. The invention aims at providing a high-precision position and attitude post-processing method. The method comprises a Kalman filtering step and a reverse R-T-S smoothing step. Horizontal velocity error and height error estimated by forward Kalman filtering is fed back to an input end of an inertial navigation solver in real time through feedback controlling parameters; inertial velocity and position divergences are inhibited, and error model linearity is improved; and reverse R-T-S smoothing is carried out on the basis, such that a POS high-precision position and attitude post-processing result is obtained. The method has the advantages that: POS application requirements are satisfied; and high-precision position and attitude information is provided for a related image processing system (such as radar or a camera) and the like.

The present invention provides a deodorizing type closet pan comprising a pedestal pan and a squatting pan, which arranges bad smelly air exhausting passage to connect with air exhausting pipe and exhausting fan outside of the urinal in the urinal or cushion plate of the pedestal plate, the bad smelly air exhausting passage comprises several air exhausting holes in the urinal or surround of the cushion plate, and air collecting passages surrounding the urinal inside and cushion plate inside; the air exhausting hole and the air collecting passage can be combined together with water flushing hole and water exhausting passage previously in the urinal, which not only exhausts water, but also sucks air. The invention has advantages that negative pressure can be produced to exhaust bad smelly air along the air exhausting passage by activating the air exhausting fan, such that smelly air is prevented to transpire from the urinal.