61 results about "Random drift" patented technology

The invention discloses a hand movement tracking system and a hand movement tracking method. The invention comprises an attitude and heading reference system based on an accelerometer, a gyroscope and a magnetic sensor, and a hand movement tracking method based on the attitude and heading reference system. The hand movement tracking method comprises the following steps: firstly, obtaining a triaxial acceleration measured by the accelerometer, a triaxial angular velocity measured by the gyroscope and a triaxial magnetic-field component measured by the magnetic sensor, performing error compensation on the magnetic sensor by adopting a least square method to establish an error model after an upper computer receives sensor data, eliminating high-frequency noise of the triaxial acceleration by virtue of a window low-pass filter, and establishing an error model for the gyroscope so as to perform error compensation on random drift of the gyroscope; secondly, effectively integrating the gyroscope, the accelerometer and the magnetic sensor by virtue of an improved adaptive complementary filtering algorithm to obtain an attitude angle and a path angle; and finally, performing gravity compensation and discrete digital integration on acceleration signals to obtain a velocity and a track of a hand movement. The tracking system and the tracking method disclosed by the invention can be applied to a man-machine interactive system, is convenient to operate, and is strong in experience feeling.

The invention discloses an MEMS (Micro Electronic Mechanical System) gyro data processing method based on wavelet threshold de-noising and an FAR (Finite Automaton Recognizable) model. For solving the problem that large amount of noise exists in the MEMS gyro output signal, the wavelet threshold de-noising method is used to process the gyro output signal to filter the noise and improve the signal-noise ratio; aiming at a static signal formed by de-noising gyro wavelet, a polynomial fitting method is used for the compensation to the gyro deterministic drift, wherein a residual error after the compensation is a random drift of the gyro, that is to say, a sample sequence required by modeling the random drift of the gyro is obtained; and aiming at modeling the random drift of the MEMS gyro at higher precision, the FAR model is used for modeling the random drift. The invention solves the problem of high output noise of the MEMS gyro, improves the signal-noise ratio and can accurately model the random drift of the gyro, thereby improving the output precision of the MEMS gyro.

The invention discloses a pedestrian indoor track positioning method. The pedestrian indoor track positioning method comprises the following steps of enabling a sensor to collect three-axis acceleration, three-axis angular speed and three-axis magnetic sensing intensity under a carrier coordinate system, and using same as data sources, wherein the three axes comprise an X axis, a Y axis and an Z axis; counting the steps, and detecting the gait; calculating the heading angle, and correcting the heading angle on the basis of main direction; when walking along a straight line, using a heuristic random drift eliminating method to correct the heading angle; when walking along a curved line, using an extended Kalman filter to compensate the offset, and correct the heading angle; according to thethree-axis acceleration, establishing a non-linear step length estimation model, and calculating the step length in the walking process; according to the step state, the number of walking steps, theheading angle and the step length, calculating the walking track and position of a pedestrian. The pedestrian indoor track positioning method has the advantages that the error in detection of step length is effectively reduced, the indoor positioning precision is improved, the problem of divergence of heading angle due to gyroscope drift is solved, and the estimation precision of a user is improved when the step length is diversified.

The invention discloses a star sensor and gyro combination attitude determination method based on SR-UKF(extended Kalman filter) filtering, and belongs to the technical field of high-precision combined attitude determination of cartographic satellite or other space vehicles. The invention aims at providing the star sensor and gyro combination attitude determination method based on SR-UKF filtering. An SR-UKF filtering algorithm is used for star sensor and gyro combination attitude determination; great improvement is realized on the existing conventional EKF filtering method. The method concretely comprises the following steps of 1, simulating the star sensor quaternion number and gyro angle speed; 2,using the error quaternion number and gyro random drift error as state variables; using theSR-UKF algorithm for realizing the fusion processing of the star sensor and gyro attitude information for filtering processing; performing feedback; possibly eliminating the error influence of the star senor and gyro error influence through iteration wave filtering processing; solving high-precision attitude information.

The present invention relates to a two-wheel self-balancing robot attitude calculation method based on the improved Extended Kalman Filter algorithm. In the prior art, the existing two-wheel self-balancing robot attitude calculation method can not well meet accuracy, real-time property, simplicity and other requirements. Baed on the problems of the existing two-wheel self-balancing robot attitude calculation method, the method of the present invention utilizes the improved Extended Kalman Filter algorithm so as to effectively combine the inertial sensor attitude measurement data, compensate the gyroscope random drift error and reduce the influence of the displacement acceleration of the two-wheel self-balancing robot on the attitude calculation during moving. The two-wheel self-balancing robot attitude calculation method of the present invention can further be simultaneously applied for the two-wheel self-balancing electric vehicle. The results of static experiments, simulated platform experiments and practical dynamic experiments of the two-wheel self-balancing robot verify that the attitude calculation accuracy of the two-wheel self-balancing robot can be increased with the method of the present invention.

The invention relates to a method for using one controller to stabilizing the holographic interference pattern, and relative device. The external disturbance will cause the random excursion of holographic interference pattern, to reduce the quality of shot holographic grating. The invention uses linear CCD to collect the interference pattern information to the controller; the controller calculates the stripe excursion and control the piezoelectric ceramic that connected to the reflector to move and process phase compensation, to reduce the excursion of interference strip, and stabilize the interference strip in exposal area, therefore, the exposal time can be prolonged significantly.

The invention relates to a wireless inclinometer while drilling, which comprises a measuring nipple skeleton. The wireless inclinometer while drilling additionally comprises a gyroscope set which is arranged on the measuring nipple skeleton. The invention provides the wireless inclinometer while drilling which has the advantages that the measurement accuracy is high, the noise interference and the random drift of gyroscopes can be eliminated and the application scope of the instrument can be widened.

The invention discloses a stable holder based on an MEMS gyroscope combination. The stable holder is composed of a class-A MEMS gyroscope sensor and a class-B gyroscope sensor. The class-A MEMS gyroscope sensor has high bandwidth and low noise. The class-B gyroscope sensor has low temperature drift and random drift. Multiple gyroscope sensors of different types are used to construct a combined sensor unit, and the advantages of different types of sensors are exerted through a reasonable control architecture and process. Therefore, the inertial measurement feedback and stability control performances are significantly improved, and the problem that the performance requirements are usually not satisfied in high-performance inertial attitude measurement and attitude stabilization is solved. The invention further discloses a control method of the stable holder based on an MEMS gyroscope combination.

The invention discloses a MIMU gyroscope random drift forecasting method based on an ARMA and BPNN combination model. The method mainly includes the steps that 1, random drift data of a single-axle micro gyroscope is collected in the static state, the stationarity of an original data sequence of a gyroscope is judged by observing graphic characteristics of an autocorrelation parameter ACF and a partial correlation parameter PACF, an ADF unit root is checked, and stationary data is obtained after a trend term of the sequence is removed through differences; 2, the stationary sequence of the gyroscope is subjected to model order determination with the AIC minimum rule method, and an ARMA model of a gyroscope drift stationary sequence is established; 3, a random drift sequence of the gyroscope is modeled through the combination model, a training set and a test set are selected from error data of the ARMA model in the step 2, and a BP neural network forecasting model is established, and the structure of a BP neural network is set; 4, sample data of the established BP neural network is trained, and the forecasting result of gyroscope random drift is preserved.

The invention discloses a method for calibrating gyro errors online based on star light information assistance in an inertial system, comprising: first, establishing a gyro error model, including a gyro installation error model, a scale factor error model and a random drift error model; then, expanding gyro errors to system state variables, and establishing a combined inertial / start light navigation Kalman filtering state equation and a measurement equation in the inertial system; finally, subjecting the gyro errors to online calibration and real-time correction during dynamic flight of an aerial vehicle to obtain inertial navigation system navigation results with the gyro errors corrected. The method enables effective utilization of star sensor high-precision altitude information during dynamic flight of an aerial vehicle so as to provide online calibration and correction of the gyro errors, improving the performance of the inertial navigation system and adapting to engineering application.

The invention discloses a random drift real-time filtering method for a fiber-optic gyroscope. Compared with the prior art, the method comprises the following steps: in a marine strap-down navigation attitude system, de-noising an acquired fiber-optic gyroscope signal in real time; decomposing data in a sliding data window according to the appointed number of layers through a second generation wavelet lifting algorithm to obtain wavelet coefficients of each layer and scale coefficients of the last layer; establishing corresponding threshold rules for the decomposed wavelet coefficients of each layer, and performing threshold quantization; combining the processed wavelet coefficients and the scale coefficients of the last layer, and progressively reconstructing the scale coefficients of each layer to obtain a de-noised signal. By virtue of the random drift real-time filtering method for the fiber-optic gyroscope, the real-time de-noising accuracy and the reaction speed of the fiber-optic gyroscope signal are effectively improved, so that attitude information errors are further suppressed. In an application process, parameters such as the width of the data window and the number of wavelet decomposing layers are easily set and can be set according to an experience value obtained by a simulation experiment.

The invention discloses a method for testing dynamic random drifting of a strap-down flexible gyroscope by the aid of a laser Doppler velocimeter, which belongs to the field of inertia technology. The method for testing dynamic random drifting of the strap-down flexible gyroscope by the aid of the laser Doppler velocimeter includes mounting the tested strap-down flexible gyroscope on a test car, and electrifying and preheating a flexible strap-down inertia unit until the flexible strap-down inertia unit is stable; synchronously acquiring data of the flexible strap-down inertia unit and data of the laser Doppler velocimeter; and keeping the test car fixed, and initially aligning fixed bases of two positions based on a course angle of 180 degrees by the aid of a Kalman filter; starting the test car, leading the car to start to run, and estimating for inertia / Doppler combined navigation for the full stroke. Accurate speed measurement of the laser Doppler velocimeter is used as observation information, random drifting errors of the flexible gyroscope in a vehicular dynamic environment are estimated by the Kalman filter, shortcomings that random drifting errors of a flexible gyroscope are only calibrated, tested and assessed by a multi-position method in a laboratory at present are overcome, and an auxiliary analysis means and an assessment basis for dynamic precision analysis of the flexible gyroscope and the flexible inertia unit are provided.

The invention discloses a method for segmentally optimal calibration to improve the measurement accuracy of air quality detection equipment, including (S10) dividing the total range of PM2.5 concentration of the SD to be corrected into at least three sub-ranges from low to high; ( S20) Control the PM2.5 concentration in the calibration room to be within each sub-range for a period of time, use the SD to be calibrated and the standard detection equipment to measure the PM2.5 concentration multiple times at the same time, and record the measurement results; (S30) Use the least square method to fit multiple PM2.5 concentration data measured in each section to obtain the optimal solution of the correction coefficient; (S40) Segmentally calibrate the PM2.5 concentration data in each section of the SD to be corrected Optimal correction factor. The present invention partitions the installation range of the equipment to be corrected, and optimally fits the correction coefficients of each partition by the least square method, thus reducing the influence of random drift errors on the measured values ​​of the equipment, and effectively avoiding some measurements The influence of the dead zone on the accuracy of the equipment, the accuracy is greatly improved.

The invention relates to an inertial measurement unit (IMU) for rotating carriers, which mainly designs a high-performance microminiature inertial instrument scheme (mainly composed of parts of three groups of orthogonal gyros formed by four MEMS gyros, three orthogonal quartz digital accelerometers, a structure body, a secondary power supply, a signal processing and communication interface, and the like), and designs the effective compensation of random drift errors of the MEMS gyros by UKF Kalman filtering. The scheme of the invention has the characteristics of wide measuring range, small size, high dynamics, and low weight, is suitable for real-time measurement of flight attitude of a high-speed rotating missile body during a flight process, suitable for position and speed measurement of long-range rocket projectiles, middle and short-range terminal guided ammunition, ground weapon attitude stabilization systems, aerial bomb flight control systems, and unmanned plane heading and attitude systems. The scheme of the invention is also applicable to nuclear submarine strapdown positioning combination, environment swing and inclination measuring systems, inertial motion recording systems of various vehicles and ships, and the like.

The invention discloses a fiber-optic gyroscope random drift compensation method for a photoelectric platform. In order to improve photoelectric platform control performances and stability, aiming at a fiber-optic gyroscope used in a feedback circuit of three gimbal axes of a photoelectric platform, the method comprises building a fiber-optic gyroscope information fusion model, ensuring a multinomial relationship of current output and history output of the fiber-optic gyroscope through primary autoregression, separating fiber-optic gyroscope random drift, ensuring a fiber-optic gyroscope random drift mathematical model through secondary autoregression, carrying out measurement through a Kalman filtering algorithm and the fiber-optic gyroscope current output amount, fusing the history output and history random drift of the fiber-optic gyroscope in a state equation and carrying out random drift on-line estimation compensation on the fiber-optic gyroscope. The method can well inhibit random drift of the fiber-optic gyroscope on the three gimbal axes of the photoelectric platform and has an important meaning for improving photoelectric platform control performances and stability.

The invention relates to ratio fluorescent nano hydrogel for pH value sensing and a preparation method thereof, belonging to the field of pH value sensing materials. The method comprises the following steps of: weighing polyurethane, bromothymol blue, coumarin 6 and Nile red according to a mass percent of 1:0.05:0.05:0.005; adding the weighed polyurethane to a mixed solution of alcohol and water with a volume ratio that the alcohol to the water is 9:1 to prepare a solution with a concentration of 500ppm; dissolving the bromothymol blue, the coumarin 6 and the Nile red together in the obtained solution and stirring for 1h; dialyzing for 24h in secondary distilled water and changing water every 4h to obtain a hydrogel suspension; and filtering the prepared suspension to obtain a product. The invention overcomes the defect that the detection sensitivity with single fluorescence intensity can be weakened because of the distribution and the concentration of probes and the random drift of an optoelectronic system and is suitable to be used as a pH value detection material in cells.

The invention discloses an MEMS gyro random drift error processing method which is characterized in that based on an existing MEMS gyro and at a signal processing back-end, measured data is analyzed by an Allan variance method, various main random error sources are effectively separated and magnitude of each error coefficient is determined; time-series modeling is conducted on the basis of checking and preprocessing the data; and finally, interference noise is effectively inhibited by multiple times of Kalman filtering processing so as to raise precision of the gyro.

The disclosed invention provides a method that exploits the correlations in the statistical properties of one inertial sensor to another. Among the dominant noise sources, the additive noise terms for different sensors are uncorrelated, however, there may be correlations between the random drift of different sensors. The method of the present invention estimates these correlations from a finite amount of calibration data in order to calculate coefficients for the optimal linear combination of multiple sensor signals. The method is a means of combining the multiple sensor outputs to obtain a virtual sensor signal with reduced drift, and in a far more time efficient and less processor intensive way than the prior art.

The invention discloses a nonlinear flexural deflection estimation method based on inertial measurement matching. Firstly, a nonlinear system state equation including an installation error angle, flexural deflection, a flexural deflection first-order derivative, an flexural deflection angle, a flexural deflection angle first-order derivative, an accelerometer constant value, random offset, a gyroscope constant value and random drift is built; secondly, based on the difference between the accelerometer measurement value of a major node and the accelerometer measurement value of a child node and the difference between the gyroscope measurement value of the major node and the gyroscope measurement value of the child node, a nonlinear system measurement equation of a system is built; finally, by using a nonlinear wave filtering method, namely a Central Difference Kalman Filter (CDKF) estimation method, the flexural deflection and the flexural deflection angle of the child node at each moment are estimated. The method has the advantages of being high in calculation precision and easy to implement, and the estimation accuracy of the flexural deflection is improved.

The invention discloses an adaptive damping control method for a wind power grid-connected system based on multiple convex polytopic bodies, and belongs to the technical field of electrical engineering. According to the method, firstly, a multi-objective robust controller is designed for a single convex polytopic body, secondly, a classification decision tree is constructed and trained with a large amount of offline data, a regression decision tree is established by using online wide-area data, and a convex polytopic body area to which a current operation point belongs is identified, and finally, a robust controller corresponding to the convex polytopic body is adaptively switched in real time according to an online decision mechanism. According to the method, when wind power output has awide range of random fluctuations, the random drift behavior of a system operating point can be effectively tracked, thus the adaptive damping control is implemented, and system oscillation is effectively damped.

The invention provides a gyroscope filtering method based on Sage-Husa Kalman filtering. A gyroscope is an important device of inertial navigation calculation in drones and is also the core element ofattitude calculation, and the attitude calculation is the foundation of follow-up speed and position calculation. A traditional filtering method uses low-pass filtering and time sequence modeling toperform Kalman filtering but has problems that low-pass filtering is severe in time delay; a noise matrix during the time sequence modeling can only represent the features of certain period of time under static conditions, reanalysis and remodeling are needed for different devices, the procedures are complex, and the time sequence modeling is not universal. The method has the advantages that the Sage-Husa self-adaptation Kalman filtering is adopted, simple modeling is performed by analyzing gyroscope features, the time sequence analysis modeling which is complex and not universal is omitted, the noise is processed as the filtering state quantity, and the random drift of the system can be well inhibited.

The invention discloses a method for inhibiting random drift errors of a vehicle-mounted MEMS gyroscope. According to the algorithm, modeling and filtering are carried out on the output data of the vehicle-mounted MEMS gyroscope sensor, so that the accuracy and stability of the output data of the vehicle-mounted MEMS gyroscope sensor are improved. The method aims at solving the problem that largeerrors exist in the output data of the vehicle-mounted MEMS gyroscope sensor. The method comprises the following steps: firstly, checking the stability of the output data of a selected MEMS gyroscopeby adopting a unit root checking method, and constructing a time sequence ARMA model through the change characteristics of an autocorrelation coefficient graph and a partial correlation coefficient graph of the output data of the MEMS gyroscope in combination with a minimum information criterion; then, applying the ARMA model to a discrete Kalman filtering equation to obtain filtered data; and finally, verifying the effectiveness of the algorithm developed by the invention through experiments. According to the method, the random error of the gyroscope is effectively suppressed, and the signal-to-noise ratio of the output signal is improved.

The invention provides an MEMS gyroscope noise estimation and filtering method aiming at the problems that an MEMS gyroscope is low in measurement precision, and random noise of the MEMS gyroscope hasuncertainty and nonlinearity. firstly, acquiring a signal with noise and preseeting and initializing a system characteristic function;then calculating a Sigma point, and constructing a statistical property coefficient at the same time; measuring and updating a one-step state prediction mean value, an error covariance matrix, a prediction mean value and a covariance matrix; observing and updatingthe minimum variance; carrying out residual variance updating by utilizing a maximum expectation algorithm and a maximum posteriori estimation criterion; and outputting the updated signal data. According to the method, a suboptimal unbiased MAP noise statistical estimation model is constructed according to a maximum posteriori estimation principle; And on the basis, a maximum expectation algorithmis introduced to convert a noise estimation problem into a mathematical expectation maximization problem, dynamic adjustment of an observation noise variance is realized, and finally estimation and filtering processing of a gyroscope random drift error are realized.

The invention discloses a dynamic environment optimization method based on a random drift particle swarm optimization algorithm, which belongs to the technical field of dynamic environment multi-objective optimization. The dynamic environment optimization method comprises the following algorithm processes of: (1) dividing an initialization particle swarm into a plurality of sub swarms by using hierarchical clustering; (2) updating the speeds and positions of all of the sub swarms; (3) calculating adaptive values of particles, evaluating and selecting an optimal particle; (4) performing overlapping detection, crowding detection and convergence detection on the sub swarms; (5) performing environmental change detection on the whole particle swarm; (6) judging whether stopping criteria for iteration are satisfied. The problems that dynamic environment optimization control variables are likely to be in a local optimal solution and the optimum value solving speed is low are solved, and the dynamic environment optimization method has the advantage of high robustness and adaptability.

The invention relates to a method for testing the influence of a vibration reduction device on the performance of an inertial navigation system and belongs to the technical field of the error calibration of inertial navigation inertial sensors. The method includes the following steps of (1) establishing a conventional gyro error model, wherein gyro errors include a gyro installation error, a scalefactor error and a random drift error, (2) considering the influence generated after the introduction of the vibration reduction device with the three types of errors and establishing a new error model, (3) providing error data for the quantitative evaluation of gyroscope performance by using the two types of error models through a calibration test, and verifying the validity of the gyroscope simulation data, and (4) analyzing the influence of an antenna attitude angle error according to the obtained error data and further analyzing the influence of the addition of a vibration isolation devices on the attitude solution. According to the method, an inertial device error model after adding the vibration reduction device is established, the influence of the vibration reduction device on theprecision of the navigation system is analyzed, and corresponding conclusions are verified through experiments and simulations.

The invention relates to a method for stabilizing random drift of a pulse laser beam. The method is used for carrying out single pulse correction on the pulse laser beam on the basis of a drift quantity moving average (MA) principle and can be used for effectively stabilizing the laser beam in time and correcting the random drift. The method is crucially characterized in that: an appropriate drift quantity correction formula y(k)=Xi.y(k-1)+Eta.x(k) is proposed, wherein k is a pulse number, x(k) is the drift quantity of the kth pulse, y(k) is the correction quantity of the kth pulse, Xi is a coefficient value obtained by simulation optimization, and Eta is a parameter related to the self characteristic of a laser device; and the formula is used to realize closed-loop real-time correction for the laser beam drift. The invention provides a rapid and accurate measure for stabilization of the pulse laser beam and has broad application prospects in the fields of laser beam control, signal detection and the like.

The invention relates to a fume collection compartment type range hood, which comprises a top frame and a side wall that protrudes downward from around the top frame. Specifically, the side wall encloses the left side, the right side and the rear side of a stove, so that the space between the stove and the top frame, and the side wall together form the fume collection compartment. Also, the inner side of the fume collection compartment is provided with an oil fume suction port, which is connected to the exhaust inlet of a ventilation motor. Thus, the fume collection compartment can limit random drift of oil fume and smell, so that the oil fume and smell generated during cooking can be completely sucked by the ventilation motor and then discharged into a collection drum or to the outside concentratedly, thus reaching the efficacy of compulsory discharge of oil fume and smell. In addition, with the invention, blow-out of stove fire by strong breeze can be avoided, or external dust can be prevented from polluting food on the stove.

The invention discloses a random four-state modulation method of a digital closed-loop fiber-optic gyroscope. The random four-state modulation method of the digital closed-loop fiber-optic gyroscope comprises the steps that a pseudo-random number generator is utilized to generate a pseudo-random number sequence, and a multi-state modulation state memory and state transition moment are established;a random modulation state selector is constructed, a random pseudo-random number is selected, and a modulation state is selected from the multi-state modulation state memory; state transition outputis completed according to the pseudo-random number and the modulation state combined with a state transition matrix; a modulation state delayer is established, a random modulation four-state phase position is generated, and based on the random modulation four-state phase position, a demodulation mark is generated in a combined mode; and the demodulation of the fiber-optic gyroscope angular rate and 2 pi voltage is completed by using the demodulation mark. According to the technical scheme of the random four-state modulation method of the digital closed-loop fiber-optic gyroscope, aiming at theproblems of gyroscope random drift and dead zones caused by electronic crosstalk between modulation signals and demodulation signals of the digital closed-loop fiber-optic gyroscope, the random four-state modulation method of the digital closed-loop fiber-optic gyroscope is adopted, the precision of the fiber-optic gyroscope can be improved, the working stability of the fiber-optic gyroscope is ensured, and the random four-state modulation method of the digital closed-loop fiber-optic gyroscope has great practical significance.